Introduction to Computational Biology: An Evolutionary Approach

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Author: Bernhard Haubold | Thomas Wiehe

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To Angelika and Claudia

Preface

In 1982, the first release of the GenBank sequence database contained 601,438 and continues to increase residues. By 2005, this number had grown beyond exponentially. Far from regarding this as “information overload”, we believe the free availability of so much precise and fundamental data on the ultimate constituents of life to be the hallmark of a golden age in biomedical research. Computational biology is concerned with helping to understand these data. The aim of this book is to give a first introduction to the computational aspects of genome-scale molecular biology, also known as genomics. The interpretation of biological data is often contingent on an understanding of the evolutionary history that has generated it. Hence, we explain evolutionary models as well as classical sequence analysis. Our intended audience is primarily students of bioinformatics, as well as researchers and students in neighboring disciplines including molecular biology, genetics, medicine, physics, mathematics, and computer science. As background, we assume familiarity with basic general and molecular biology as well as elementary probability theory. We also expect an interest in computers and their programming. In writing this book we have benefited from the expertise and support of a number of colleagues. Clemens Beckstein invited us in 1999 to give our first lecture series on computational biology at Jena University. Wolfgang Stephan and Monty Slatkin encouraged us to turn the lecture notes accumulated in Jena into a textbook. Steffi Gebauer-Jung helped with some of the algorithms we present. Claudia Acquisti, Frank Leßke, Peter Pfaffelhuber, Karl Schmid, and Daniel Zivkovic commented on earlier versions of the manuscript. Our students improved our teaching of computational biology over the years. Finally, we owe a huge debt of gratitude to Angelika Börsch-Haubold, who edited the entire book, compiled the index and guided this project through the production stage. Without her contribution there would be no book. Freising and Köln, March 2006

Bernhard Haubold Thomas Wiehe

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Reading and Writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Design and Scope of This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Sequences in Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Sequences in Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 3 4 7

Part I Sequences in Space 2

Optimal Pairwise Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 What Is an Alignment? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Biological Interpretation of the Alignment Problem . . . . . . . . . . . . . 2.3 Scoring Alignments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Amino Acid Substitution Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 PAM Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 BLOSUM Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.3 Comparison between PAM and BLOSUM . . . . . . . . . . . . . . 2.4.4 Application of Substitution Matrices . . . . . . . . . . . . . . . . . . . 2.5 The Number of Possible Alignments . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Global Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Shotgun Sequencing and Overlap Alignment . . . . . . . . . . . . . . . . . . 2.8 Local Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.9 Accommodating Affine Gap Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.10 Maximizing vs. Minimizing Scores . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.11 Example Application of Global, Local, and Overlap Alignment . . . 2.12 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.13 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.14 Exercises and Software Demonstrations . . . . . . . . . . . . . . . . . . . . . . .

11 14 15 15 16 18 22 25 27 27 30 33 35 36 38 39 39 40 40

X

Contents

3

Biological Sequences and the Exact String Matching Problem . . . . . . . 3.1 Exact vs. Inexact String Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Na¨ıve Pattern Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 String Searching in Linear Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Set Matching Using Keyword Trees . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Suffix Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Suffix Tree Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 Suffix Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.9 Repetitive Sequences in Genomics—the -value Paradox . . . . . . . 3.10 Detection of Repeated and Unique Substrings Using Suffix Trees . 3.11 Maximal Repeats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.12 Generalized Suffix Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.13 Longest Common Substring Problem . . . . . . . . . . . . . . . . . . . . . . . . . -Mismatches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.14 3.15 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.16 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.17 Exercises and Software Demonstrations . . . . . . . . . . . . . . . . . . . . . . .

43 43 44 45 46 48 51 54 55 56 57 59 59 60 60 62 62 63

4

Fast Alignment: Genome Comparison and Database Searching . . . . . . 4.1 Global Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Local Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Global/Local Alignment: -Error Matching . . . . . . . . . . . . . 4.2.2 Examples of Database Search Programs . . . . . . . . . . . . . . . . 4.3 Database Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Heuristic vs. Optimal Alignment Methods . . . . . . . . . . . . . . . . . . . . . 4.5 Application: Determining Gene Families . . . . . . . . . . . . . . . . . . . . . . 4.6 Statistics of Local Alignments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.1 Maximum Local Alignment Scores . . . . . . . . . . . . . . . . . . . . 4.6.2 Choosing a Substitution Matrix . . . . . . . . . . . . . . . . . . . . . . . 4.7 Bit Scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.9 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.10 Exercises and Software Demonstrations . . . . . . . . . . . . . . . . . . . . . . .

65 67 69 71 73 79 79 79 81 81 84 85 85 86 86

5

Multiple Sequence Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.1 Scoring Multiple Alignments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 5.2 Multiple Alignment by Dynamic Programming . . . . . . . . . . . . . . . . 94 5.3 Heuristic Multiple Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.5 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.6 Exercises and Study Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Contents

XI

6

Sequence Profiles and Hidden Markov Models . . . . . . . . . . . . . . . . . . . . 6.1 Profile Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Hidden Markov Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Profile Hidden Markov Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Exercises and Software Demonstration . . . . . . . . . . . . . . . . . . . . . . .

101 101 106 111 113 114 114

7

Gene Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 What is a Gene? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Computational Gene Finding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Measuring the Accuracy of Gene Predictions . . . . . . . . . . . . . . . . . . 7.4 Ab initio Methods: Searching for Signals and Content . . . . . . . . . . . 7.4.1 Codon Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 Finding Splice Sites with a Sequence Profile . . . . . . . . . . . . 7.4.3 Exon Chaining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Comparative Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.1 General Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.2 Comparative Gene Prediction at the Adh Locus . . . . . . . . . . 7.6 Problems and Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.8 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.9 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

117 117 118 121 124 126 126 131 134 134 135 138 139 139 140

Part II Sequences in Time 8

Phylogeny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Is There a Tree?—Statistical Geometry . . . . . . . . . . . . . . . . . . . . . . . 8.2 Likelihood-Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 The Number of Possible Phylogenies . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Distance Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.1 Average Linkage Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.2 Neighbor-Joining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Maximum Parsimony . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6 Maximum Likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.7 Searching Through Tree Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.7.1 Nearest Neighbor Interchange . . . . . . . . . . . . . . . . . . . . . . . . 8.7.2 Subtree Pruning and Regrafting . . . . . . . . . . . . . . . . . . . . . . . 8.7.3 Branch and Bound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.8 Bootstrapping Phylogenies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.10 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.11 Exercises and Study Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

143 145 146 148 150 152 155 157 159 161 162 163 163 164 166 167 167

XII

9

10

Contents

Sequence Variation and Molecular Evolution . . . . . . . . . . . . . . . . . . . . . . 9.1 The Record of Past Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Mutations and Substitutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 The Molecular Clock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Explicit Models of Molecular Evolution . . . . . . . . . . . . . . . . . . . . . . 9.5 Estimating Evolutionary Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.6 Coding Sequences: Synonymous and Non-Synonymous Substitutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.7 Substitutions in Globin Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . .................................... 9.8 Applications of 9.8.1 A Language Gene? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.8.2 Selection in the Human Genome . . . . . . . . . . . . . . . . . . . . . . 9.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.10 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.11 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

169 170 171 172 173 175

Genes in Populations: Forward in Time . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Polymorphism and Genetic Diversity . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 The Neutral Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Modeling Evolution Forward in Time . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 The Neutral Wright-Fisher Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.1 Fixation and Loss of Alleles . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.2 The Hardy-Weinberg Law . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.3 Fixation Probability and Time to Fixation . . . . . . . . . . . . . . . 10.4.4 Loss of Genetic Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5 Adding Mutation to the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5.1 Finite Alleles Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5.2 Infinite Alleles Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5.3 Infinite Sites Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6 Mutation Drift Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6.1 The Rate of Fixation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6.2 Number of Alleles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6.3 Genetic Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.7 Sampling Alleles from Populations . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.7.1 Ewens’ Sampling Formula . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.7.2 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.8 Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.10 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.11 Exercises and Software Demonstration . . . . . . . . . . . . . . . . . . . . . . .

187 187 191 193 194 195 197 197 200 201 202 203 203 203 203 205 207 209 209 212 212 214 215 215

177 180 182 182 183 184 185 185

Contents

XIII

11

Genes in Populations: Backward in Time . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Individuals’ Genealogies vs. Gene Genealogies . . . . . . . . . . . . . . . . 11.2 Forward vs. Backward in Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 The Coalescent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4 Coalescent vs. Phylogenetic Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5 The Infinite Sites Model and the Number of SNPs . . . . . . . . . . . . . . 11.6 Mathematical Properties of the Neutral Coalescent . . . . . . . . . . . . . . 11.6.1 Tree Depth, Tree Size and the Number of Segregating Sites 11.6.2 Heterozygosity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6.3 The Distribution of Segregating Sites . . . . . . . . . . . . . . . . . . 11.7 Simulation Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.8 Recombination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.9 Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.10 Combining Recombination and Selection . . . . . . . . . . . . . . . . . . . . . 11.11 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.12 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.13 Software Demonstrations and Exercises . . . . . . . . . . . . . . . . . . . . . . .

217 217 218 222 224 224 225 225 232 233 233 233 237 238 242 242 242

12

Testing Evolutionary Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1 Hudson-Kreitman-Aguadé (HKA) Test . . . . . . . . . . . . . . . . . . . . . . . 12.2 Tajima’s Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3 Fu and Li’s Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4 McDonald-Kreitman Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.5 Minimum Number of Recombination Events . . . . . . . . . . . . . . . . . . 12.6 Detecting Linkage Disequilibrium . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.7 Implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.9 Exercises and Software Demonstration . . . . . . . . . . . . . . . . . . . . . . .

245 245 248 251 253 253 255 257 257 257

A

bioinformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.1 Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.1.1 Protein Substitution Matrices . . . . . . . . . . . . . . . . . . . . . . . . . A.1.2 Number of Alignments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.1.3 Pairwise Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.2 Match . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.2.1 String Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.2.2 Suffix Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.2.3 Repeat Searching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.2.4 Hash Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.2.5 Dotplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.3 Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.3.1 Hidden Markov Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.4 Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.4.1 Phylogeny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.4.2 Drift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

259 259 259 261 262 263 263 264 265 265 266 266 267 268 268 270

XIV

Contents

A.4.3 Wright-Fisher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 A.4.4 Coalescent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 B

Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275

C

Molecular Biology Figures and Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279

D

Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285

Answers to Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313 Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321 Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323

1 Introduction The chromosome structures are at the same time instrumental in bringing about the development they foreshadow. They are law-code and executive power—or, to use another simile, they are architect’s plan and builder’s craft—in one. Erwin Schrödinger [220, p. 22]

Since the discovery of chromosomal inheritance in the early 20-th century it has fascinated biologists that in contrast to, say, rocks or clocks, living organisms carry with them their own miniaturized part list, the genome. This specifies in its DNA sequence the primary structure of all proteins that make an organism as well as the primary structure of catalytically active RNA molecules. From 1995 onward, a rapidly growing number of genomes of free-living organisms have been sequenced in their entirety. This means that the succession of all bases, or base pairs (bp) since DNA is double stranded, of the organisms concerned is known. Figure 1.1 shows a sample of 106 organisms whose genomes have been sequenced. It contains representatives of all three domains of life, archebacteria (archaea), bacteria, and eucaryotes, which include humans. Organisms that are mentioned elsewhere in this book are marked by an arrow. There is a heavy bias toward bacteria, which is partly due to their medical bp for importance. Another reason is that bacterial genomes are small, e.g. the human pathogen Haemophilus influenzae, and hence easier to sequence than for bp). example the human genome, which is more than 1,000 times larger ( Reading and writing of the genome form the molecular basis of life.

1.1 Reading and Writing In every organism constant reading of the genome by the molecular genetic machinery of the cell is fundamental to sustaining its vital processes. The concomitant flow of information from DNA to proteins, but never in the reverse direction, is summarized by the well-known central dogma of molecular biology (Fig. 1.2). Computational biology is traditionally concerned with two questions that build directly on the central dogma: where are the genes and what are their functions? In fact, these questions can be understood as an attempt to reproduce in silico the molecular genetic machinery of a cell. The enzymes making up this machinery locate specific genes with great precision. The subsequent expression of a particular gene takes place in a context of hundreds or even thousands of other genes active in that

2

1 Introduction Encephalitozoon cuniculi Plasmodium yoelii Plasmodium falciparum Caenorhabditis elegans Drosophila melanogaster Mus musculus Homo sapiens

Schizosaccharomyces pombe

E

Saccharomyces cerevisiae

Neurospora crassa Oryza sativa Thermoplasma acidophilum Halobacterium sp Methanosarcina mazei Methanosarcina acetivorans Methanobacterium thermoautotr Methanopyrus kandleri Pyrococcus abyssi Pyrococcus horikoshii Pyrococcus furiosus Methanococcus jannaschii Archaeoglobus fulgidus Pyrobaculum aerophilum Sulfolobus solfataricus Aeropyrum pernix

Arabidopsis thaliana

A

Synechocystis sp Anabaena variabilis Chlamydophila caviae Chlamydia muridarum Chlamydia trachomatis Chlamydophila pneumoniae Chlamydia pneumoniae Borrelia burgdorferi Treponema pallidum Leptospira interrogans Fusobacterium nucleatum Clostridium tetani Clostridium perfringens Clostridium acetobutylicum Streptococcus mutans Streptococcus pneumoniae

Streptococcus pyogenes Streptococcus agalactiae Lactococcus lactis Lactobacillus plantarum Listeria monocytogenes Listeria innocua Enterococcus faecalis Staphylococcus epidermidis Staphylococcus aureus Bacillus subtilis Bacillus cereus Bacillus anthracis Helicobacter pylori Helicobacter hepaticus Campylobacter jejuni Mycoplasma pulmonis Ureaplasma urealyticum Mycoplasma penetrans Mycoplasma pneumoniae Mycoplasma genitalium

Mycoplasma gallisepticum Chlorobium tepidum Bacteroides thetaiotaomicron Rickettsia prowazekii Rickettsia conorii Caulobacter crescentus Bradyrhizobium japonicum Sinorhizobium meliloti Mesorhizobium loti Brucella melitensis Agrobacterium tumefaciens Ralstonia solanacearum Nitrosomonas europaea Neisseria meningitidis Xylella fastidiosa Xanthomonas campestris Xanthomonas axonopodis Pseudomonas syringae Pseudomonas putida Pseudomonas aeruginosa Coxiella burnetii Shewanella oneidensis Vibrio vulnificus

B

Vibrio parahaemolyticus Vibrio cholerae

Glossina brevipalpis Yersinia pestis

Salmonella typhi Salmonella typhimurium Shigella flexneri Escherichia coli

Wigglesworthia glossinidia Buchnera aphidicola

Haemophilus influenzae

Pasteurella multocida Deinococcus radiodurans Streptomyces coelicolor Streptomyces avermitilis Mycobacterium leprae Mycobacterium tuberculosis Mycobacterium bovis Corynebacterium glutamicum Bifidobacterium longum Thermotoga maritima

Fig. 1.1. Phylogeny showing the three domains of life: E: eucaryotes; A: archebacteria; B: bacteria. Computed from the ribosomal RNA sequences of 106 organisms whose genomes have been sequenced completely. Organisms mentioned in this book are marked by an arrow.

1.2 Design and Scope of This Book

3

Selection

Replication

Selection

Transcription DNA

Translation RNA

Protein

Rev. Transcription

Fig. 1.2. Reading and writing at the machine level of life. Solid lines constitute the central dogma and correspond to “reading” from the genome. Dashed and dotted lines correspond to “writing” into the genome through reverse transcription and—on an evolutionary timescale— selection, respectively.

cell. It is this context of expression that constitutes to a large extent the function of a gene. Given that there is enough information contained in a genome to largely determine the shape of a bacterial cell or of a human being, we might ask, how this information gets written into the genome. The answer is that some of it is written directly by reverse transcription (Fig. 1.2), which leads to the apparently excessive size of many eucaryotic genomes, including our own. However, more important for the preservation of useful information is a process that operates on a very different time scale from the molecular mechanisms mentioned so far: evolution by natural selection, which interacts with proteins and, perhaps to a lesser extent, transcripts (Fig. 1.2). The interaction between natural selection and these molecules is indirect: an organism carrying a protein that functions better than the original “wild type” leaves more offspring, who in turn leave even more offspring and as a result the advantageous information thrown up initially by a random mutation spreads throughout the population. It is this subtle yet intimate relationship between the reading aspect of the genome, which is the traditional subject of molecular biology and hence computational biology, and its writing aspect, usually the preserve of evolutionary biologists, which has motivated us to include evolutionary models in this book together with more classical sequence analysis methods.

1.2 Design and Scope of This Book This book is divided in two parts. The first part deals with sequences in (sequence) space, the second with sequences in (evolutionary) time. We have also included a Glossary of important terms and Appendices on four topics:

4

1 Introduction

1. Appendix A describes the Software bioinformer, which we have written to visualize some of the ideas treated in Parts I and II; 2. Appendix B briefly surveys important stochastic concepts that are used extensively in computational biology; 3. Appendix C deals with the chemical nature of nucleic acids and amino acids; 4. Appendix D contains pointers to software and databases useful in the practice of computational biology. In addition, a web-page with supplementary material is posted at http://adenine.biz.fh-weihenstephan.de/icb/ 1.2.1 Sequences in Space Amino acid and nucleotide sequences are often pictured as points in sequence space [173, 58], the metaphor underlying Part I. In sequence space individual sequences are ordered according to their distances measured in terms of the number of mutational steps necessary to get from one sequence to another. Let us start with the simplest case, a DNA “sequence” of length 1. There are four such sequences possible, A C G T , and the corresponding sequence space can be visualized as a three-dimensional simplex or a tetrahedron (Fig. 1.3). Starting from any one of the G T A

C

Fig. 1.3. Tetrahedral sequence space for DNA sequences of length 1.

nucleotides, we can reach all three others in a single step. The sequence space that accommodates all DNA sequences of length 2 consists of a tetrahedral hyperspace where each vertex of the hyper-tetrahedron is occupied by a simple tetrahedron. As before, the edges represent single mutational steps, i.e. the exchange of one residue by another. Figure 1.4 shows a projection of this space onto two dimensions. Each vertex in one of the four simple tetrahedrons is connected to three vertexes within the structure as well as to the corresponding vertexes in the other three tetrahedrons. For instance, vertex AA in the top tetrahedron is connected to CA, TA, and GA within the tetrahedron, as well as to AG, AC, and AT in the three other tetrahedrons. Each of the edges in this six-dimensional space has the same length. In order to depict DNA sequences of length 3 in sequence space, we construct a hyper-tetrahedron with vertexes consisting of the structure depicted in Figure 1.4. Each sequence is connected to six other sequences within the basic tetrahedron as


5

GA TA CA AA

GC TC CC

GG

AC

GT

TG

TT CG

AG

CT AT

Fig. 1.4. Sequence space for DNA sequences of length 2.

well as to three other sequences at corresponding positions in the other three tetrahedrons. Figure 1.5 depicts the underlying nine-dimensional space, which represents the mutational distances between the 64 codon triplets. Sequence space has two important properties: (i) it is highly connected and (ii) it has a high dimensionality. The connectedness means that within the space for sequences of length the largest distance between any two points is . The high dimensionality is a precondition for the connectedness. The number of dimensions necessary for constructing sequence space, , is

where is the size of the alphabet over which the sequence is formed. In our DNA example and hence the dimensionality of the codon space is , as we have already observed in Figure 1.5. The sequence space describing all possible sequences of 1 kb, the length of a vertexes. In this vast space typical gene in Escherichia coli, contains only a very small proportion of vertexes is occupied by actually existing sequences. A larger number of sequences, albeit still vanishingly few compared to the total number of possibilities, correspond to functional genes. Many different functions can be encoded in 1 kb. However, sequences with similar functions tend to cluster in sequence space, forming clouds of functional variants in a sea of lethal or as yet evolutionarily untested configurations. The fact that close neighbors in sequence space tend to have similar functions motivates the sequence comparison methods discussed in Part I.

6

1 Introduction

Fig. 1.5. DNA sequence space for DNA sequences of length 3, i.e. for all 64 codons.


7

We start off in Chapter 2 with the treatment of pairwise alignment using dynamic programming methods. These methods are effective but practical pairwise alignment algorithms reduce the time and memory requirement of dynamic programming by combining them with exact matching, the topic of Chapter 3. In Chapter 4 dynamic programming is wedded with exact matching to yield fast pairwise alignment algorithms. Pairwise alignments are a special case of multiple alignments, which are dealt with in Chapter 5. The information contained in a multiple sequence alignment can be used to classify protein sequences into protein families, of which there are far fewer than actual protein sequences. The tools used for this classification, profile analysis and hidden Markov models, are the topics of Chapter 6. Alignments, profiles, as well as hidden Markov models, are used in various combinations to predict the location of genes in as yet unannotated DNA sequences. Our introduction to gene prediction in Chapter 7 concludes Part I. 1.2.2 Sequences in Time Part II is concerned with sequence evolution. Sequences change over time through mutation, which corresponds to a random walk in sequence space. Given the sheer infinite size of this space, it may come as a surprise that anything as useful as the set of instructions specifying the molecular parts of a human being could ever be found therein. However, we have already explained that in principle few mutational steps are necessary to get from any given sequence to any other. In addition, selection provides a powerful signpost at every vertex in sequence space, thereby guiding the mutational random walk. The condition for the evolvability of a sequence was made precise in 1970 by the English evolutionary biologist Maynard Smith [173]. He noted that for evolution by natural selection to occur, sequence space needs to be traversable in unit mutational steps without passing through non-functional intermediates. We have already seen that a sequence of length is embedded in an -dimensional space. In other words, it has neighbors that can be reached by a single mutational step. The condition for evolution by natural selection is that for any given functional sequence there exists at least one functional onemutation neighbor. If we call the fraction of viable single-step mutations a given sequence can change into, the condition for evolvability simply amounts to [173]

Since many mutations are known to have no effect on fitness, i.e. to be neutral, this condition will often be met [221]. Chapter 8 introduces what is perhaps the best-known aspect of sequence evolution, the reconstruction of phylogenies. Such reconstruction is based on the differences between homologous sequences observed in a multiple sequence alignment. These differences are the topic of Chapter 9. In particular, we shall be interested in differences found between protein-coding sequences, as these can help us detect selection, the force that keeps sequences on the straight and narrow in sequence space. The analysis of phylogenies as well as the analysis of differences in coding sequences

8

1 Introduction

are usually based on inter-species comparisons. In Chapter 10 we zoom in on a given species and concentrate on intra-species sequence comparisons. To be more precise, we shall concentrate on the dynamics of genes found in one population of organisms. The traditional method to trace the history of genes in populations moves forward in time and this is also the perspective we start off with in Chapter 10. A reverse in time perspective on evolution was developed in the late 1970s and early 1980s. Today it is the standard way to think about genes in populations and Chapter 11 introduces the corresponding methodology, known as coalescent theory. One of the central concerns of any investigation of genes in time is the detection of selection. Without it, there would be no function, that is no phenotype attached to the sequence genotype. Chapter 12 makes this concern explicit by describing a number of formal hypothesis tests that can be applied to sequence data in order to detect selection.

Part I Sequences in Space

sdfsdf

2 Optimal Pairwise Alignment So now we are going to sequence the human genome and then we will sequence the chimp genome. Once this is done, we align each gene from the two organisms and the gene that is left over must be the language gene. If humans have it, we call it the Chomsky gene. If chimps have it, we call it the Chimsky gene. Sydney Brenner [26]

Living organisms are the product of an evolutionary history. This has important consequences for the practice of biology, whether of the organismal or of the molecular kind. In classical biology, organs that derive from a recent common ancestor are called homologous. For instance, a human hand is homologous to a chimpanzee’s hand, a cat’s paw, and a bat’s wing. These structures are characterized by common anatomical features and similar, albeit highly diverse, functions. In contrast, the wings of bats and flies are not homologous and anatomically very different, even though they are both organs of flight. Such organs are said to be analogous. To put matters simply, homology tends to imply similarity of function, while similarity of function may be due either to analogy or to homology. In molecular biology the search for homologous traits, in this case residues in DNA or protein sequences, reappears in the guise of the alignment problem. As explained in detail below, two sequences are aligned by writing homologous residues on top of each other. This may or may not work well, i.e. the resulting alignment may or may not be convincing. If the two proteins can be aligned reliably, they tend to be similar. Moreover, they tend to have similar functions. The fact that similar sequences encode similar functions is the reason why we start this book with alignment algorithms: such algorithms are used for finding similar sequences of known function and thereby efficiently determine to a first approximation the function of novel genes. Alignments can be either pairwise, comprising just two sequences, or multiple, comprising an arbitrary number of sequences. Pairwise alignments might be regarded as a special case of multiple alignments. In practice, however, the computational complexity of aligning multiple sequences is such that the corresponding algorithms are usually not straight extensions of the pairwise approaches. Instead, multiple alignments are often constructed by repeatedly merging pairwise alignments. In this chapter we concentrate on basic pairwise alignments, while multiple alignments are the topic of Chapter 5.

12

2 Optimal Pairwise Alignment

There are numerous approaches to aligning two sequences, but three of the most important strategies are known as global, local, and overlap (Fig. 2.1). A global alignment algorithm is appropriate if two sequences are homologous across their entire length. However, many sequences only share homologous regions while other parts of the molecule are quite unrelated. Sequence regions that correspond to a functional unit are also known as domains. For example, an important class of developmental genes, the Hox genes, were first discovered in the fruit fly Drosophila melanogaster. There they act as transcriptional regulators that determine the identity of body segments along the anterior-posterior axis of the developing embryo. D. melanogaster has eight Hox genes located on chromosome 3. The remarkable thing about these genes is that their layout along the chromosome corresponds to the layout of larval proto-segments in which they are most highly expressed. Today, Hox genes have been discovered in almost all animals. In mammals, for example, they occur in four complexes, which each correspond to one of the Drosophila Hox clusters. Again, the polarity of the genes’ layout along the mammalian chromosomes reflects the anterior-posterior ordering of their expression in the early embryo. All Hox genes are characterized by a conserved region of 60 amino acids, the homeodomain, which binds to specific regulatory motifs on the DNA. Figure 2.2 displays a global alignment of the two Hox proteins from human and D. melanogaster that are expressed in the most anterior segments of the embryo. Homologous amino acids are written on top of each other, deleted amino acids are indicated by gaps ( ). Notice that a deletion in one sequence implies an insertion in the other. Hence insertions and deletions are often referred to as “indels”. As shown in Figure 2.2, the annotated homeodomain has remained highly conserved over the approximately 590 million years [45, p. 389] that separate the protostomes, to which D. melanogaster belongs, from the vertebrates. In contrast, regions outside the homeodomain have changed beyond recognition. A local alignment algorithm is appropriate for the discovery of specific conserved regions such as homeodomains.

Global

Local

Overlap Fig. 2.1. Types of pairwise alignment discussed in this chapter. Homologous regions are indicated in black.


13

Overlap alignments, finally, are used to align sequences where only the ends match. This is the case for example in the context of the assembly of sequences from a genome sequencing project. MMDVSSMYGNHPHHHHPHANAYDGYSTTTASAANASSYFAPQQHQPHLQLQQQQQ .MDNARMN.........SFLEYPILSSGDSGTCSARAYPS...............

55 30

HQHLQQPQQHLTYNGYESSSPGNYYPQQQAQLTPPPTSSHQVVQQHQQQQQAQQQ .DHRITTFQSCAVSANSCGGDDRFLVGRGVQIGSP....................

110 64

QLYPHSHLFSPSAAEYGITTSTTTGNPGTPLHPSSHSPADSYYESDSVHSYYATA HHHHHHHHHHPQPATY...Q..TSGNLGVSYSHSSCGPS...YGSQNFSAPYSPY

165 111

AVATVAPPSNSSPITAANASATSNTQQQQQQAAIISSENGMMYTNLDCMYPTAQA ALN...........QEADVSGG...........................YPQC..

220 126

QAPVHGYAGQIEEKYAAVLHASYAPGMVLEDQDPMMQQATQSQMWHHQQHLAGSY .AP.................................................AVY

275 131

ALDAMDSLGMHAHMHHGLPHGHLGNLANNPHQQQPQVQQQQQQPHQQPQHPQNQS SGNLSSPMVQHHHHHQGYAGGAVG...................SPQYIHHSYG..

330 165

PAAHQQHHQNSVSPNGGMNRQQRGGVISPGSSTSSSTSASNGAHPASTQSKSPNH ....QEHQSLALATYN.................N.SLSPLHASHQEACRSPASET

385 198

SSSIPTYKWMQLKRNVPKPQAPSYLPAPKLPASGIASMHDYQMNGQLDMCRGGGG SSPAQTFDWMKVKRNPPK.....................................

440 216

GGSGVGNGPVGVGGNGSPGIGGVLSVQNSLIMANSAAAAGSAHPNGMGVGLGSGS ......TGKVGEYG...................................YLG...

495 227

Homeodomain GLSSCSLSSNTNNSGRTNFTNKQLTELEKEFHFNRYLTRARRIEIANTLQLNETQ ..Q........PNAVRTNFTTKQLTELEKEFHFNKYLTRARRVEIAASLQLNETQ

550 272

Homeodomain VKIWFQNRRMKQKKRVKEGLIPADILTQHSTSVISEKPPQQQQPQPPELQLKSQG VKIWFQNRRMKQKKREKEGLLPISPATPPGNDEKAEESSEKSSSSP......... SDLGGNELATGAPSTPTTAMTLTAPTSKQS ...........CVPSPGSSTSDTLTTSH..

605 318

635 335

Fig. 2.2. Global alignment of two orthologous Hox proteins with annotated homeodomain. : homeotic labial protein (Lab) from Drosophila melanogaster; : homeobox protein Hox-A1 from human; white on gray: identical amino acids; black on gray: similar amino acids; : gaps, i.e sequence insertions/deletions.

Whether global, local, or overlap, alignments can be computed by two types of algorithms: optimal and heuristic. Historically, optimal alignment algorithms were developed first (Table 2.1) and are based on a computational technique known as “dy-

14


namic programming”. This guarantees that the best possible alignment is found given the parameters chosen. Heuristic algorithms, on the other hand, can be thought of as fast procedures for approximating their optimal counterparts. Heuristic approaches to alignment are the topic of Chapter 4. The first optimal alignment algorithm was Table 2.1. Landmarks in the development of pairwise alignment algorithms. Year 1970 1981 1988 1990 1997

Authors Needleman & Wunsch Smith & Waterman Pearson & Lipman Altschul et al. Altschul et al.

Algorithm Global Local FASTA BLAST Gapped BLAST

Approach Optimal Optimal Heuristic Heuristic Heuristic

published in 1970 [189], while the first widely used heuristic alignment algorithm, the FASTA algorithm, was published over a decade later [197]. In this chapter we explain how dynamic programming is used to compute global, local, and overlap alignments (Fig. 2.1). We begin by clarifying further what is meant by an alignment.

2.1 What Is an Alignment? DNA sequences and the protein sequences they encode change in evolutionary time through mutation. The simplest types of mutation are point mutations and insertions/deletions, also known as indels. These two types of change are modeled by classical alignment algorithms. Say we wish to align the two DNA sequences AACGT and ACCGTT. This is done by writing their residues on top of each other such that either two residues are paired, corresponding to presence or absence of a point mutation, or a residue is paired with a gap, corresponding to an insertion or deletion. It is not permitted to pair a gap with a gap. The reason for this is not that two sequences cannot inherit the same deletion, they certainly can. However, there is not enough information to infer such a course of evolution from just two sequences. Figure 2.3 displays six example alignments that can be generated by following these rules. The 1 2 3 4 5 6 AACGT- -AACGT A-ACGT AACGT------ AA-CGT- -A-A-C-G-TACCGTT ACCGTT ACCGTT -----ACCGTT A-CCGTT A-C-C-G-T-T Fig. 2.3. Six of the 3,653 possible ways to align the DNA sequences AACGT and ACCGTT.

question is, which of the possible alignments is the “best”?

2.3 Scoring Alignments

15

Intuitively, we might prefer alignment 1 with its four matched residues to alignment 2, which contains only a single matched residue. However, alignment 5 also contains four matches. On the other hand, it contains three gaps, whereas alignment 1 contains only one gap, leaving us still with the feeling that alignment 1 is best. How can we turn this intuition into an objective evaluation procedure?

2.2 Biological Interpretation of the Alignment Problem In order to choose the “best” alignment, we need to recall that, biologically speaking, we seek to align homologous residues. In addition, we assume that evolution is parsimonious. In other words, when calculating an alignment we aim to minimize the number of evolutionary changes implied by an alignment. Consider again the alignments in Figure 2.3. The first one implies that since divergence from their last common ancestor the two sequences have undergone one point mutation and one insertion/deletion. Hence, the alignment implies two evolutionary events. In contrast, the second alignment implies five such events: one insertion/deletion and four point mutations. Therefore, we would prefer the first alignment to the second. Instead of minimizing the number of evolutionary events, it is conceptually equivalent to maximize a score that reflects the similarity of the two sequences. All alignment algorithms surveyed in this chapter are based on such maximizing scoring schemes and we will explain why this is so later on in Section 2.10.

2.3 Scoring Alignments The minimal unit of an alignment consists of one position from each of the two sequences being compared. When scoring such a pair of positions it is customary to distinguish between pairs consisting of two residues and pairs that form part of an indel. As to indels, the simplest way to model them is known as the linear gap model. Under this model the score of a gap, , is defined as

where is the length of the gap and and the gap extension cost. However, molecular biologists have known for a long time that gaps often extend over several residues. In other words, the scoring of the existence of an indel needs to be distinguished from the scoring of its length. This idea is embodied in the widely used affine gap model, according to which the score for a gap is computed as (2.1) denotes gap opening. A reasonable combination of parameters could be and , i.e., gap opening is penalized more than twice as heavily as gap extension [10]. where

16


When scoring residues, it depends on whether nucleotides or amino acids are considered. Nucleotides are often modeled as all having the same rate of mutation. purine or pyrimidine pyrimiThis is a simplification, as transitions (purine purine), but in dine) are usually more frequent than transversions (pyrimidine practice this model works quite well. For simplicity we distinguish here only between matched and mismatched nucleotides. A mismatch might be scored as -3 and a match as +1 [10]. In contrast to nucleotides, different amino acids mutate at very different rates. To be more precise, it is the codons specifying the amino acid sequence that mutate and it is clear that the number of mutational steps necessary for converting one amino acid into another can range from one to three (cf. Table C.4). This variation in mutational distance is one reason for the different mutation rates among amino acids. More important, however, are their highly diverse physico-chemical properties (Fig. C.4), which result in substitutions that disrupt protein structure to very different degrees. Whatever the underlying molecular causes might be, the fact of differing amino acid mutation rates is routinely described in terms of substitution matrices. These possible changes between amino acids and are disgive scores for all cussed next.

2.4 Amino Acid Substitution Matrices Today, there are far more nucleotide than protein sequence data available. This reflects the fact that genomes are bigger than the proteomes they encode. However, protein sequencing started before the discovery of the double helical structure of DNA in 1953. Between 1949 and 1955 the first complete protein sequence, that of bovine insulin, was determined by Fred Sanger and coworkers [129, p. 212]. It was only once Sanger and Maxam and Gilbert independently devised simple methods for DNA sequencing in the mid 1970s that an organism’s nucleotide genotype became much more accessible than its protein phenotype. For the early development of alignment algorithms proteins were therefore the focus of research. Molecular biologists often talk about “mutations in protein sequences”. This is shorthand for saying that the sequence encoding the protein has mutated. Due to the degenerate nature of the genetic code, the mutational distance between amino acids varies from one to three steps. Figure 2.4 shows the 20 proteinogenic amino acids in their codon space, which was already displayed in Figure 1.5. Consider, for example the amino acid methionine (Met), which is encoded by a single codon, ATG. Threonine (Thr), which is encoded by the four codons ACA ACC ACG ACT AC[ACGT], is separated by one mutational step from methionine. Glycine (Gly), which is encoded by GG[ACGT], is two mutations removed. Histidine (His, CA[TC]), finally, is an example of an amino acid that can only be reached by changing all three nucleotides encoding methionine. Apart from the differences in mutational distances separating the amino acids, they also differ widely in their physico-chemical properties. Figure C.4 shows that

2.4 Amino Acid Substitution Matrices

17

Fig. 2.4. The 20 proteinogenic amino acids in codon space (Fig. 1.5). Starting from

Met ,

Thr can be reached in one mutational step in the underlying codon, Gly in two steps and His in three.

one half of amino acid side chains are classified as polar, the other half as nonpolar. Moreover, the size of the side chains ranges from tiny in glycine, where it consists of a single hydrogen atom, to bulky in tryptophan, with its aromatic tworing side chain. Additional physico-chemical categories that distinguish the twenty proteinogenic amino acids include negative and positive charge and aliphatic vs. nonaliphatic. Given the diversity of amino acids in mutational distance as well as chemical properties, a simple match/mismatch scoring scheme is not sufficient. There is an additional fundamental aspect of biological sequences that the match/mismatch scheme ignores: evolutionary time. Two sequences that are being compared might have diverged for only a few thousand years. In this case a homologous pair of residues is less likely to be mismatched than after tens of millions of

18


years. Substitution matrices are designed to take both the diversity of amino acids, as well as the evolutionary dimension of sequence comparison into account. Two series of amino acid scoring matrices are in common use today, the PAM and the BLOSUM series. 2.4.1 PAM Matrices The first widely used scoring scheme for aligned amino acids was devised in the 1970s by Margret Dayhoff and coworkers [46]. In her model of protein evolution Dayhoff incorporated the observation that pairs of amino acids mutate at different rates, i.e. are differentially conserved. For instance, as shown in Table 2.2, lysine (K) is 37 times more likely to mutate into its physico-chemical cousin arginine (R) than into, say, the chemically more different leucine (L, Fig. C.4). Apart from physicochemical similarity, the rate of amino acid mutation depends on the divergence time of the sequences concerned. In the limit of zero divergence time, two sequences are identical and the probabilities of any mutations are all zero. This would correspond to all entries in Table 2.2 being zero except for the values on the main diagonal, which would be 10,000, i.e. correspond to a probability of 1. In contrast, in the limit of infinite divergence time, any amino acid is equally likely to have mutated into any other. In this case all entries in Table 2.2 would be equal to the background frequencies of the amino acids labeling the rows. Therefore, a time-graded series of substitution matrices is necessary for aligning pairs of protein sequences that are separated by different divergence times. Dayhoff’s starting point for accounting for sequence conservation over time were mutation alignments of closely related proteins from which all possible probabilities were computed. These probabilities were normalized such that they corresponded to a change in 1% of amino acid positions (Fig. 2.5). In other words, the original probabilities were normalized in order to correspond uniformly to comparisons of sequences separated by 1 Percent Accepted Mutations, or 1 PAM. “Accepted” refers to the fact that these mutations have passed the filter of selection, as opposed to those mutations that never made it into an extant population.

300 amino acids

Fig. 2.5. Pairs of aligned protein sequences differ to varying degrees. Hence the need to normalize the mutation probabilities computed from such alignments. : difference; : mutation.


19

Table 2.2. Matrix of amino acid mutation probabilities (times 10,000), also known as a state transition matrix. Large numbers on the diagonal indicate a high probability of no change. For example, the probability of an alanine mutating into an arginine (A R) is AR , while the reverse mutation probability (R A) is RA . Data taken from [46].

A R N D C Q E G H I L K M F P S T W Y V A 9867 2 9 10 3 8 17 21 2 6 4 2 6 2 22 35 32 0 2 18 1 9913 1 0 1 10 0 0 10 3 1 19 4 1 4 6 1 8 0 1 R N 4 1 9822 36 0 4 6 6 21 3 1 13 0 1 2 20 9 1 4 1 6 0 42 9859 0 6 53 6 4 1 0 3 0 0 1 5 3 0 0 1 D C 1 1 0 0 9973 0 0 0 1 1 0 0 1 0 0 5 1 0 3 2 Q 3 9 4 5 0 9876 27 1 23 1 3 6 4 0 6 2 2 0 0 1 10 0 7 56 0 35 9865 4 2 3 1 4 1 0 3 4 2 0 1 2 E G 21 1 12 11 1 3 7 9935 1 0 1 2 1 1 3 21 3 0 0 5 1 8 18 3 1 20 1 0 9912 0 1 1 0 2 3 1 1 1 4 1 H 2 2 3 1 2 1 2 0 0 9872 9 2 12 7 0 1 7 0 1 33 I L 3 1 3 0 0 6 1 1 4 22 9947 2 45 13 3 1 3 4 2 15 2 37 25 6 0 12 7 2 2 4 1 9926 20 0 3 8 11 0 1 1 K 1 1 0 0 0 2 0 0 0 5 8 4 9874 1 0 1 2 0 0 4 M F 1 1 1 0 0 0 0 1 2 8 6 0 4 9946 0 2 1 3 28 0 P 13 5 2 1 1 8 3 2 5 1 2 2 1 1 9926 12 4 0 0 2 S 28 11 34 7 11 4 6 16 2 2 1 7 4 3 17 9840 38 5 2 2 22 2 13 4 1 3 2 2 1 11 2 8 6 1 5 32 9871 0 2 9 T W 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 9976 1 0 Y 1 0 3 0 3 0 1 0 4 1 1 0 0 21 0 1 1 2 9945 1 V 13 2 1 1 3 2 2 3 3 57 11 1 17 1 3 2 10 0 2 9901

The divergence time implied by 1 PAM varies strongly depending on the set of organisms we are looking at. In a comparison of 32 proteins from D. melanogaster and D. obscura, 1.91 non-synonymous substitutions were found per non-synonymous years [168, p. 191]. Since there are approximately two non-synonymous site per sites per amino acid, this implies that 1 PAM corresponds to

million years. However, in the hominoid lineage substitution rates are lower. A comparison of 97 protein coding genes from human and chimpanzee uncovered 0.006 non-synonymous substitutions per non-synonymous site [261]. Assuming human/chimpanzee divergence time of 5.5 million years, 1 PAM corresponds to

million years in this case, 1.75 times more than in Drosophila. For sequences diverged by 1 PAM, Table 2.2 shows the probabilities with which each of the 20 amino acids mutated into any of the other 19 amino acids or remained unchanged. In order to obtain the probability matrix appropriate for an evolutionary distance of PAM, the original probability matrix only needs to be taken to the power of . In this way substitution probabilities for arbitrary evolutionary distances can be computed.

20


Notice the distinction between the number of mutations a stretch of 100 amino acids has accepted, and the percent difference between sequences. For example, an evolutionary distance of 160 PAM happens to correspond to an average difference be the probability of amino of 70% (Fig. 2.6). In order to calculate this, let

100

%-Difference

80

60

40

20

0

0

100

200

PAM

300

400

500

Fig. 2.6. Expected %-difference between two protein sequences as a function of their evolutionary distance expressed in units of PAM (solid line). Dotted line: equality between PAM number and %-difference; Dashed line: asymptotic %-difference for infinite PAM.

acid mutating into amino acid over an evolutionary distance of 160 PAM. The corresponding percent difference is computed as

were is the frequency of the -th amino acid in the data set from which the original PAM matrix was constructed. This is also referred to as the background frequency of the amino acid. The percent difference will always be less or equal to the PAM number, because mutations may affect the same amino acid more than once, which is increasingly likely to happen with growing PAM numbers. Notice also that the percent difference between two sequences must lie between 0% and 100%, while the number of mutations a sequence has accepted varies between 0 and infinity.


21

However, a PAM matrix like PAM160 depicted in Table 2.3, contains scores rather than probabilities. The substitution probabilities appropriate for an evolutionTable 2.3. PAM160 amino acid substitution matrix. Match scores are shown in bold. A R N D C Q E G H I L K M F P S T W Y V

A 2 -2 0 0 -2 -1 0 1 -2 -1 -2 -2 -1 -3 1 1 1 -5 -3 0

R -2 6 -1 -2 -3 1 -2 -3 1 -2 -3 3 -1 -4 -1 -1 -1 1 -4 -3

N 0 -1 3 2 -4 0 1 0 2 -2 -3 1 -2 -3 -1 1 0 -4 -2 -2

D 0 -2 2 4 -5 1 3 0 0 -3 -4 0 -3 -6 -2 0 -1 -6 -4 -3

C -2 -3 -4 -5 9 -5 -5 -3 -3 -2 -6 -5 -5 -5 -3 0 -2 -7 0 -2

Q -1 1 0 1 -5 5 2 -2 2 -2 -2 0 -1 -5 0 -1 -1 -5 -4 -2

E 0 -2 1 3 -5 2 4 0 0 -2 -3 -1 -2 -5 -1 0 -1 -7 -4 -2

G 1 -3 0 0 -3 -2 0 4 -3 -3 -4 -2 -3 -4 -1 1 -1 -7 -5 -2

H -2 1 2 0 -3 2 0 -3 6 -3 -2 -1 -3 -2 -1 -1 -2 -3 0 -2

I -1 -2 -2 -3 -2 -2 -2 -3 -3 5 2 -2 2 0 -2 -2 0 -5 -2 3

L -2 -3 -3 -4 -6 -2 -3 -4 -2 2 5 -3 3 1 -3 -3 -2 -2 -2 1

K -2 3 1 0 -5 0 -1 -2 -1 -2 -3 4 0 -5 -2 -1 0 -4 -4 -3

M F -1 -3 -1 -4 -2 -3 -3 -6 -5 -5 -1 -5 -2 -5 -3 -4 -3 -2 2 0 3 1 0 -5 7 0 0 7 -2 -4 -2 -3 -1 -3 -4 -1 -3 5 1 -2

P 1 -1 -1 -2 -3 0 -1 -1 -1 -2 -3 -2 -2 -4 5 1 0 -5 -5 -2

S T 1 1 -1 -1 1 0 0 -1 0 -2 -1 -1 0 -1 1 -1 -1 -2 -2 0 -3 -2 -1 0 -2 -1 -3 -3 1 0 2 1 1 3 -2 -5 -3 -3 -1 0

W Y V -5 -3 0 1 -4 -3 -4 -2 -2 -6 -4 -3 -7 0 -2 -5 -4 -2 -7 -4 -2 -7 -5 -2 -3 0 -2 -5 -2 3 -2 -2 1 -4 -4 -3 -4 -3 1 -1 5 -2 -5 -5 -2 -2 -3 -1 -5 -3 0 12 -1 -6 -1 8 -3 -6 -3 4

ary distance of PAM are converted into scores in two steps. First they are divided by the background frequency of the original amino acid. For example, the probability of mutating from alanine to arginine (A R), AR , is divided by the background frequency of A, A , while the probability of mutating in the reverse direction (R A), RA , is divided by the background frequency of R, R . Thus for any pair of amino : acids , , we define the ratio

Whereas the probability matrix shown in Table 2.2 is not symmetrical, i.e. , it holds that . This is important, as the direction of a mutation is usually unknown when we try scoring a given pair of amino acids. Figure 2.7 shows the original amino acid background frequencies used in the construction of the original PAM matrices in addition to a set of background frequencies computed from a recent release of the protein database SwissProt. The amino acid frequencies in both data sets vary, ranging over almost an order of magnitude with tryptophan always being the rarest amino acid at 1%. The most frequent amino acid is glycine (8.9%) in the old data set and leucine (9.6%) in the more recent version. Perhaps surprisingly, the frequencies of tyrosine and phenylalanine have remained virtually unchanged.

22

2 Optimal Pairwise Alignment 100 90 80

1000

60

Frequency

70

50 40 30 20 10 0 Gly Ala Leu Lys Ser Val Thr Pro Gly Asp Arg Phe Asn Gln Ile His Cys Tyr Met Trp Amino Acid

Fig. 2.7. Background frequencies of amino acids. The filled bars show the data used for the construction of the original PAM matrices in the 1970s [46]. The open bars display the amino acid frequencies calculated from the 172,233 protein sequences totaling 62,615,309 amino acids contained in the SwissProt database, release 46.2 of 2004.

The second step in converting transition probabilities into scores consists of tak: ing the logarithm base 2 of

The final entries, round , are known as log-odds scores. PAM matrices were standard for aligning proteins until the early 1990s. They are still in use today, but have frequently been replaced by the BLOSUM system log-odds matrices [114]. 2.4.2 BLOSUM Matrices BLOSUM stands for BLOcks SUbstitution Matrices, because these matrices are derived from the BLOCKS database [113] consisting of blocks of ungapped protein alignments [113]. An example entry of the BLOCKS database is shown in Figure 2.8. For deriving the entries in a BLOSUM matrix, consider an alignment of sepairs of amino acids at each column of an alignment of quences. We can form


23

ID CLAUDIN1; BLOCK AC IPB003548C; distance from previous block=(3,4) DE Claudin-1 signature BL PR01377; width=8; seqs=3; 99.5%=501; strength=972 CLD1_HUMAN|O95832 ( 152) MTPVNARY 83 CLD1_MOUSE|O88551 ( 152) LTPINARY 100 CLD1_RAT|P56745 //

( 152) MTPVNARY

83

Fig. 2.8. Example entry in the BLOCKS database [202]. The sequences are taken from rat, mouse, and human claudin1. Claudins are a family of transmembrane proteins that form a major component of tight junctions [7, p.1068]. A BLOCKS entry consists of a four-line header and a sequence part. In the sequence part each line consists of the following elements: The SwissProt identifier, followed by the bracketed first position in the sequence included in the block, the actual amino acid sequence, and finally a sequence-weight. The latter is scaled such that 100 identifies the sequences that are most distant from all the other sequences contained in the block. Clusters are separated by a blank line.

sequences. Let be the number of pairs of amino acids and ; then the observed frequency of this pair is

where is the length of the alignment. The frequency of this pair expected to occur by chance alone is

where is the observed frequency of amino acid . Entry in a BLOSUM matrix is then calculated as the log-odds of finding amino acids and at a homologous position in an alignment vs. the probability of observing this pair by chance alone:

This is then multiplied by 2 and rounded to the nearest integer to yield the actual entries in the score matrix: round By way of an example, we now compute a BLOSUM matrix based on the single BLOCK shown in Figure 2.8. It consists of a total of 24 amino acids occurring at frequencies shown in Figure 2.9A. If for the time being we ignore clustering, we obtain the expected probabilities of finding pairs of amino acids shown in Figure 2.9B. Notice that the expectations of unobserved pairs of amino acids are set to zero, as they are ignored in subsequent steps of the derivation. There is a total of 24 pairs of amino acids in the block and Figure 2.10 shows their raw counts and frequencies. By dividing entries in Figure 2.10B by the entries

24


A R N I L M P T Y V

A Count 3 3 3 1 1 2 3 3 3 2

B 1/8 1/8 1/8 1/24 1/24 1/8 1/8 1/8 1/8 1/12

A R N I L M P T Y V

A 1/64 0 0 0 0 0 0 0 0 0

R 1/64 0 0 0 0 0 0 0 0

N

I

L

A

M

P

T

Y

V

1/64 0 1/576 0 0 1/576 0 0 1/144 1/144 0 0 0 0 1/64 0 0 0 0 0 1/64 0 0 0 0 0 0 1/64 0 1/144 0 0 0 0 0 1/144

Fig. 2.9. The frequency of amino acid , , in the alignment block shown in Figure 2.8 without clustering. A: Amino acid counts and probabilities; B: probabilities of pairs of amino acids, ; probabilities of unobserved pairs of amino acids, e.g. RA, are set to zero.

A R N I L M P T Y V

A 3 0 0 0 0 0 0 0 0 0

A RNILAMPTYV 3 0 0 0 0 0 0 0 0

3 0 0 0 0 0 0 0

0 0 0 0 0 0 2

0 2 0 0 0 0

1 0 0 0 0

3 0 3 0 0 3 0 0 0 1

A R N I L M P T Y V

A 1/8 0 0 0 0 0 0 0 0 0

R

N

I

L

1/8 0 0 0 0 0 0 0 0

1/8 0 0 0 0 0 0 0

0 0 0 0 0 0 1/8

0 1/8 0 0 0 0

B A

1/24 0 0 0 0

M

P

T

Y V

1/8 0 1/8 0 0 1/8 0 0 0 1/24

Fig. 2.10. Observed pairs of amino acids in the alignment block shown in Figure 2.8. A: Raw counts; B: frequencies.

in Figure 2.9B and taking the logarithm to the base 2, we get Figure 2.11A. Multiplication by 2 and rounding to the nearest integer gives us finally the substitution matrix in Figure 2.11B. If we base our computations on the two clusters that make up the block in Figure 2.8, the expected probability of finding an amino acid is computed as the sum of the two probabilities of finding it in either cluster. For instance, the probability of (Fig. 2.12A). As to the observed pairs finding a M is of amino acids, we now make only comparisons between clusters. Hence, there are a total of 16 pairs as listed in Figure 2.12B.


A R N I L M P T Y V

A 3.0 -

A R N I L A M P T Y V 3.0 -

3.0 -

3.6

3.6 -

2.6 -

A R N I L M P T Y V

3.0 - 3.0 - - 3.0 - - - 2.6

A 6 -

B RNILAMPTYV 6 -

6 -

7

7 -

5 -

3 - 3 - - 3 - - - 5

Fig. 2.11. BLOSUM matrix computed from the alignment block shown in Figure 2.8. A: B: . See text for details.

A R N I L M P T Y V

A Count 3 3 3 1 1 2 3 3 3 2

1/8 1/8 1/8 1/32 1/32 3/32 1/8 1/8 1/8 3/32

A R N I L M P T Y V

A 2 0 0 0 0 0 0 0 0 0

25

;

B RNILAMPTYV 2 0 0 0 0 0 0 0 0

2 0 0 0 0 0 0 0

0 0 0 0 0 0 1

0 1 0 0 0 0

1 0 0 0 0

2 0 2 0 0 2 0 0 0 1

Fig. 2.12. Clustered analysis of the alignment block shown in Figure 2.8. A: Count of amino acid and its probability, ; B: Observed pairs of amino acids.

2.4.3 Comparison between PAM and BLOSUM Recall that the PAM series of substitution matrices is constructed by extrapolating from a single starting matrix of substitution probabilities (Fig. 2.2). In contrast, BLOSUM matrices are based on frequencies of pairs of amino acids obtained from alignment blocks of various degrees of conservation. A BLOSUM62 matrix (Table 2.4), for instance, is derived from substitution probabilities based on alignments where all those sequences with identity are grouped and given the same weight as each of the ungrouped sequences. Hence, in contrast to PAM matrices, where a high PAM number corresponds to high sequence divergence, in the BLOSUM system high numbers correspond to a high degree of conservation and Table 2.5 shows examples of pairs of similar matrices from the BLOSUM and PAM series. Having introduced substitution matrices, let us state more formally the definition of an alignment score. We consider an alphabet which contains the gap character.

26


Table 2.4. BLOSUM62 amino acid substitution matrix. Match scores are shown in bold. A R N D C Q E G H I L K M F P S T W Y V

A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0

R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3

N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3

D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3

C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1

Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2

E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2

G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3

H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3

I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3

L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1

K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2

M F P -1 -2 -1 -1 -3 -2 -2 -3 -2 -3 -3 -1 -1 -2 -3 0 -3 -1 -2 -3 -1 -3 -3 -2 -2 -1 -2 1 0 -3 2 0 -3 -1 -3 -1 5 0 -2 0 6 -4 -2 -4 7 -1 -2 -1 -1 -2 -1 -1 1 -4 -1 3 -3 1 -1 -2

S T 1 0 -1 -1 1 0 0 -1 -1 -1 0 -1 0 -1 0 -2 -1 -2 -2 -1 -2 -1 0 -1 -1 -1 -2 -2 -1 -1 4 1 1 5 -3 -2 -2 -2 -2 0

W Y V -3 -2 0 -3 -2 -3 -4 -2 -3 -4 -3 -3 -2 -2 -1 -2 -1 -2 -3 -2 -2 -2 -3 -3 -2 2 -3 -3 -1 3 -2 -1 1 -3 -2 -2 -1 -1 1 1 3 -1 -4 -3 -2 -3 -2 -2 -2 -2 0 11 2 -3 2 7 -1 -3 -1 4

Table 2.5. Examples of corresponding matrices from the BLOSUM and PAM series of amino acid substitution matrices [114]. BLOSUM PAM 45 250 62 160 80 120

Then any two members , of this alphabet have a score attached. Further, and and consider the sequences and their gap-containing versions (cf. Figure 2.3). Then the score denote by of an alignment of these two sequences of length is simply the sum of the scores of pairs of characters: Score Notice that this formula implies that each position in an alignment evolves independently. In many instances, for example in the case of protein coding sequences, this is an oversimplification, which nevertheless works remarkably well.

2.5 The Number of Possible Alignments

27

2.4.4 Application of Substitution Matrices Consider an alignment of the polypeptides AHY and GHF, for example AHY GHF According to the BLOSUM62 matrix (Table 2.4) the score of this alignment is . Substitution matrices such as BLOSUM62 are tuned to a specific evolutionary distance between the sequences being compared. Since over time any given position in a sequence is increasingly likely to have been hit by a mutation, PAM matrices with increasing PAM numbers become effectively more tolerant of non-identical amino acids and the same is true for BLOSUM matrices with decreasing BLOSUM numbers. Figure 2.13 shows this effect for optimal local alignments of the two Hox genes already shown in Figure 2.2. The alignment based on BLOSUM80 is sharply centered on the homeodomain. The alignment based on BLOSUM62 is extended by 24 amino acids at the C-terminus, while the N-terminus has remained unchanged. The alignment based on BLOSUM45, finally, has the same C-terminus as the BLOSUM62 alignment, but is extended by 386 amino acids at the N-terminus.

2.5 The Number of Possible Alignments Now that we know how to score alignments the easiest way to decide our original question of which alignment is best might be to simply enumerate all possible alignments and look for the one with the highest score. Before embarking on such a “na¨ıve” algorithm, let us estimate the number of possible alignments. As before, and , we consider two sequences as the number of alignments that can be formed between and further define them. The key insight necessary for calculating the number of possible alignments is that any alignment ends in one of exactly three ways:

Consider the effect of these three possible ends on the number of alignments that can be formed out of the remaining residues in the alignment. The ending removes one residue from sequence

,

removes one residue from sequences

and each, and finally, the ending removes one residue from sequence . Therefore we can write the following recursion: (2.2) where the righthand side lists the number of alignments corresponding to each of the three types of ending. In addition to recursion (2.2) we need a “stop criterion”, also known as boundary condition:

28


BLOSUM80 Homeodomain NSGRTNFTNKQLTELEKEFHFNRYLTRARRIEIANTLQLNETQVKIWFQNRRMKQ NAVRTNFTTKQLTELEKEFHFNKYLTRARRVEIAASLQLNETQVKIWFQNRRMKQ

562 284

Homeodomain KKRVKEGLIP KKREKEGLLP

572 294

BLOSUM62 Homeodomain NSGRTNFTNKQLTELEKEFHFNRYLTRARRIEIANTLQLNETQVKIWFQNRRMKQ NAVRTNFTTKQLTELEKEFHFNKYLTRARRVEIAASLQLNETQVKIWFQNRRMKQ

562 284

Homeodomain KKRVKEGLIPADILTQHSTSVISEKPPQQQQPQP KKREKEGLLPISPATPPGNDEKAEESSEKSSSSP

596 318

BLOSUM45 SAAEYGITTSTTTGNPGTPLHPSSHSPADSYYESDSVHSYYATAAVATVAPPSNS SFLEYPILSSGDSGTCSARAYPSDHRI-----------TTFQSCAVSANSCGGDD

176 51

SPITAANASATS------NTQQQQQQAAIISSEN-GMMYTNLDC--MYPTAQAQA RFLVGRGVQIGSPHHHHHHHHHHPQPATYQTSGNLGVSYSHSSCGPSYGSQNFSA

222 106

PVHGYAGQIEEK----YAAVLHASYAPGMVLEDQDPMMQQATQSQMWHHQQHLAG PYSPYALNQEADVSGGYPQCAPAVYSGNL----SSPMVQ--------HH------

273 143

SYALDAMDSLGMHAHMHHGLPHGHLGNLANNPHQQQPQVQQQQQQPHQQPQHPQN --------------HHHQGYAGGAVGS---------PQYIH-----HSYGQEHQS

328 170

QSPAAHQQHHQNSVSPNGGMNRQQRGGVISPGSSTSSSTSASNGAHPASTQSKSP LALAT----YNNSLSP---LHASHQEACRSPASETSS---------PAQ------

383 203

NHSSSIPTYKWMQLKRNVPKPQAPSYLPAPKLPASGIASMHDYQMNGQLDMCRGG -------TFDWMKVKRNPPK-----------------------------------

438 216

GGGGSGVGNGPVG-VGGNGSPGIGGVLSVQNSLIMANSAAAAGSAHPNGMGVGLG --------TGKVGEYGYLGQP----------------------------------

492 229

Homeodomain SGSGLSSCSLSSNTNNSGRTNFTNKQLTELEKEFHFNRYLTRARRIEIANTLQLN ---------------NAVRTNFTTKQLTELEKEFHFNKYLTRARRVEIAASLQLN

547 269

Homeodomain ETQVKIWFQNRRMKQKKRVKEGLIPADILTQHSTSVISEKPPQQQQPQP ETQVKIWFQNRRMKQKKREKEGLLPISPATPPGNDEKAEESSEKSSSSP

596 318

Fig. 2.13. Optimal local alignments of two Hox proteins based on different substitution matrices. : homeotic labial protein (Lab) from D. melanogaster; : human Hox-A1; white on gray: identical amino acids; black on gray: similar amino acids.

2.5 The Number of Possible Alignments

29

(2.3) When trying to evaluate recursion (2.2), we have two options: (i) direct top down or (ii) indirect bottom up computation. We consider the top down approach first. It , and is called top down, because it starts from the desired end-result, say then works its way “downwards” by repeated application of Equation (2.2), until the boundary condition (2.3) is reached. Figure 2.14 depicts this recursive process as a tree to be read from the top. Convince yourself from this graphic that the number of possible global alignments of two sequences of length 2 is 13 by summing all the terms in the tree corresponding to the boundary condition (2.3).

Fig. 2.14. Top down computation of the number of possible alignments for two sequences each of length 2 according to Equation (2.2).

Notice that appears three times in Figure 2.14, which means that this term is evaluated three times. In order to avoid such inefficient duplication of computational effort, we need to change our point of view. In the top down approach just discussed, the number of possibilities for the shortest alignments is computed last. If we compute them first and save these results as input for the computation of the instead of exnext partial solution, the runtime of the algorithm becomes ponential. To illustrate this, we start with an empty results matrix (Fig. 2.15A). This is initialized using boundary condition (2.3) by filling the first row and first column with 1’s (Fig. 2.15B). The interpretation of this step is that there is only one way of aligning any sequence with a sequence of length zero, which consists exclusively of gaps. Now we can fill in the empty rows in our matrix by repeated application of recursion (2.2) going from left to right (or filling in columns of cells going from top to (Fig. 2.15C). Notice that bottom). Hence, we quickly get the result that the results matrix is symmetrical. This corresponds to our intuition that the number of possible alignments does not depend on the way the two sequences concerned are ). labeled ( The idea used to speed up the computation of the number of possible alignments is to store subproblem solutions. When we augment this strategy by an optimization step, we arrive at the important computational method known as dynamic programming.

30

2 Optimal Pairwise Alignment A 012 0 1 2

B 012

C 01 2

0 111 1 1 2 1

0 11 1 1 13 5 2 1 5 13

Fig. 2.15. Calculating the number of possible alignments from two sequences of length 2 by storing subproblem solutions. A: Empty solutions matrix; B: initialized solutions matrix— cells summed to compute the adjacent empty cell are shown in bold; C: filled in solutions matrix.

2.6 Global Alignment In the subsequent sections three types of optimal pairwise alignment algorithms are dealt with: global, overlap, and local, which were first introduced at the beginning of this chapter (Fig. 2.1). All three types of alignment are computed using variations on the same underlying dynamic programming algorithm. Each algorithm is based on a two-dimensional table similar to those shown in Figure 2.15, which is known as a dynamic programming matrix. This data structure is subjected to the following three steps: 1. initializing of the dynamic programming matrix; 2. filling in the dynamic programming matrix with scores of optimal partial alignments; 3. extracting one or more alignments from the dynamic programming matrix; this is also called traceback. We start by working through an example of global alignment, before stating the relevant rules more formally. This is followed by a briefer treatment of overlap and local alignments, which turn out to be simple extensions of the global alignment algorithm. Our example consists of the two sequences ACCGTT and AGTTCA throughout, which we wish to align using as scoring scheme , , and . In other words, we use a linear gap model. The affine gap model is incorporated into the algorithm in Section 2.9. Nomenclature We need a certain amount of vocabulary when analyzing sequences. To a computer scientist the sequences of molecular biologists are strings. A string is a list of characters ordered from left to right. The following notational conventions are used in this and subsequent chapters: 1. 2. 3. 4.

is the length of . is a substring of . is a prefix of . is a suffix of .

2.6 Global Alignment

Characters in strings are labeled

31

. As an example we consider ADDISABEBA

In this case , ADD is a prefix, IS is a substring, and BA is a suffix. We will often use “string” and “sequence” interchangeably. Notice, however, that a subsequence is different from a substring. While a substring comprises only characters that are contiguous in the parent string, a subsequence may contain non-contiguous elements. For example, DSB is a subsequence, but not a substring of ADDISABEBA. Initializing and , we write each sequence In order to align our two example sequences, preceded by a gap character along the two dimensions of the dynamic programming along the horizontal axis and along the vertimatrix shown in Figure 2.16, , as the score of the optimal cal axis. We define the partial optimal score, and . Since there is no alignment that alignment of the prefixes . To find , we note that there is only consists of two aligned gaps, a single way of writing the corresponding alignment, , which has a score of -1. In general, there is only a single way of aligning a sequence exclusively with gaps and, hence, we can easily fill in the first row and also the first column of the dynamic programming matrix (Fig. 2.16).

– A C C G T T 0 1 2 3 4 5 6 – A G T T C A

index of

0 1 2 3 4 5 6

0 -1 -2 -3 -4 -5 -6 -1 -2 -3 -4 -5 -6

index of first row of

first column of

Fig. 2.16. Initializing the dynamic programming matrix for global alignment.

Filling in the Dynamic Programming Matrix In order to calculate the remaining values of , we need to consider the three ways in which an existing alignment can be extended by one unit. We have already encountered this in Section 2.5 when calculating the number of possible alignments: . These three possibilities correspond to three moves in the

32


dynamic programming matrix: horizontal for aligning a residue from with a gap, with diagonal for aligning two residues, and vertical for aligning a residue from a gap (Fig. 2.17). The aim is to find the maximum of the three possible values of implied by the three possible extensions of an alignment. In this way the entire dynamic programming matrix is filled going from left to right and from top to bottom (Fig. 2.18). This takes the same amount of time as computing the number of . possible alignments in Section 2.5: – 0 – A G T T C A

A C C G T T 1 2 3 4 5 6

0 0 -1 1 -1 2 -2 3 -3 4 -4 5 -5 6 -6

-2 -3 -4 -5 -6

Fig. 2.17. Dynamic programming matrix for global alignment: an empty cell surrounded by three filled cells can be evaluated by considering the three possible extensions of an alignment indicated by the three arrows and selecting the configuration with the maximum score. In the case shown, the three values to choose from are (i) , (ii) , and (iii) . Hence the cell is filled with as shown in Figure 2.18.

– A C C G T T 0 1 2 3 4 5 6 – A G T T C A

0 1 2 3 4 5 6

0 -1 -2 -3 -4 -5 -6

-1 1 0 -1 -2 -3 -4

-2 0 0 -1 -2 -1 -2

-3 -1 -1 -1 -2 -1 -2

-4 -2 0 -1 -2 -2 -2

-5 -3 -1 1 0 -1 -2

-6 -4 -2 0 2 1 0

Fig. 2.18. Filled in dynamic programming matrix for global alignment. The score of the optimal alignment can be read from the bottom right cell; in this example it is 0.

The score in the bottom righthand corner of Figure 2.18 is the maximum score. In our example this happens to be 0, but it could be any number, positive or negative.

2.7 Shotgun Sequencing and Overlap Alignment

33

Notice how the repeated application of simple rules has yielded this result, even though there are 8,989 possible global alignments to choose from. To summarize the strategy for filling in the global alignment matrix, we define and as the score of the optimal alignment between

The values of

where

are calculated through the recursions

is the gap extension score.

Traceback Having obtained the optimal score, we need to extract the corresponding alignment. This is usually done by a process referred to as “traceback”. At each step of the forward phase of the algorithm outlined above the cell in the dynamic programming matrix from which the current value has been derived is “remembered” through a back pointer. We visualize this as a little arrow that points to the cell from which the entry in the current cell was computed (Fig. 2.19). There might be up to three back pointers per cell corresponding to three cooptimal alignment extensions. Once the dynamic programming matrix has been filled in, the back pointers are followed from the bottom righthand corner to the top lefthand corner and the corresponding alignment is constructed in reverse order (Fig. 2.19). There is only one possible traceback path in Figure 2.19, which means that the corresponding global alignment is unique. This is not necessarily the case and if more than one traceback path is possible, there is no way to distinguish the resulting cooptimal alignments under the model. In practice, most implementations ignore this problem and return an arbitrary member of the potentially large set of cooptimal global alignments. Next we consider overlap alignment (cf. Fig. 2.1), which is applied in the context of a widely used sequencing strategy known as shotgun sequencing.

2.7 Shotgun Sequencing and Overlap Alignment There are two possibilities for sequencing large pieces of DNA: (i) “Walk” along the target sequence in steps of a couple of hundred nucleotides. This is slow as a new primer needs to be synthesized for every step in the walk. Moreover, walking can

34


– A G T T C A

0 1 2 3 4 5 6

– 0

A 1

C 2

C 3

G 4

T 5

T 6

0 -1 -2 -3 -4 -5 -6

-1 1 0 -1 -2 -3 -4

-2 0 0 -1 -2 -1 -2

-3 -1 -1 -1 -2 -1 -2

-4 -2 0 -1 -2 -2 -2

-5 -3 -1 1 0 -1 -2

-6 -4 -2 0 2 1 0

ACCGTT-A--GTTCA Fig. 2.19. Dynamic programming matrix with back pointers. The optimal global alignment (bottom) corresponds to the path marked in bold (top).

only be carried out sequentially, as the primer sequence necessary for a given step is unknown until the previous step has been taken. An alternative approach is (ii) shotgun sequencing, where random fragments of the target sequence are cloned into a vector and sequenced using always the same primers complementary to the vector. Shotgun sequencing [215] is motivated by the fact that each DNA sequencing reaction identifies only between 400 and 800 nucleotides, a very small portion of even small genomes (Table 2.6). With the shotgun approach many sequencing reactions can be carried out in parallel. The resulting rapidly generated fragments overlap and we will explain how this fact can be used to assemble the target sequence. But before that we ask, how many such fragments are necessary for successful assembly? Let be the length of the target sequence. When sequencing random bases from that sequence, the probability of not sequencing a specific nucleotide is

where is the number of bases sequenced. The complementary probability that a base has been sequenced is where is called the coverage. Hence, with six-fold coverage of a genome, 99.8% of nucleotides are included in the data set ready for computational assembly. The idea underlying the assembly of shotgun fragments is known as overlap alignment, sometimes also referred to as end space free alignment [99, ch. 11]. With this approach, gaps at either ends of the alignment are not penalized (Fig. 2.1). This is done by initializing the first row and column of the dynamic programming matrix to zero and then filling the matrix exactly as for the global alignment (Fig. 2.20). For a linear gap model the corresponding recursions are

2.8 Local Alignment

35

The traceback step starts from the maximum score in the last row or column and the positions left unaccounted for are filled with gaps. Notice that the cell containing on the traceback path in Figure 2.20 contains three back pointers, imthe entry plying that there are three cooptimal overlap alignments of the two input sequences. Moreover, in contrast to global alignments, there might be more than one cooptimal starting point for the traceback. Table 2.6. Milestones of genome sequencing; bp: base pair, Mb: pairs. Year 1978 1982 1995 1996 1998 2000 2000 2001/2003 2002 2005

Organism bacteriophage X174 bacteriophage Haemophilus influenzae Saccharomyces cerevisiae Caenorhabditis elegans Arabidopsis thaliana Drosophila melanogaster Homo sapiens Mus musculus Pan troglodytes

Genome Size 5386 bp 48.502 bp 1.8 Mb 12 Mb 97 Mb 120 Mb 180 Mb 3.1 Gb 2.5 Gb 3.1 Gb

base pairs, Gb:

base

Commentary first genome [214] shotgun sequencing [215] first free-living organism [78] first eucaryote [88] first metazoan [243] first plant [245] first metazoan with whole-genome shotgun [3] aim of the human genome project [128, 252, 39] comparative pharmacogenomics [40] closest relative of humans [223]

2.8 Local Alignment Two proteins often share only homologous domains rather than homology across their entire lengths. In this case we seek a local, rather than a global alignment as illustrated in Figure 2.1. Put slightly more formally, the local alignment problem is solved by finding a maximally scoring pair of substrings. This requirement increases the space of possible alignments, since all pairs of substrings need ACT, for example, contains the substrings to be considered. The sequence A C T AC CT ACT . In general, a sequence of length contains substrings. For two sequences of lengths and there are correspondingly pairs of substrings to be compared. Na¨ıvely, we would need to compute a global alignment for each one of these and then look for the highest scoring pair. However, a slight variation on the global alignment scheme ensures that the time requirement . This variafor local alignments is equal to that for global alignments, i.e. tion consists in taking the maximum over the three possible extensions of an existing alignment and zero (Fig. 2.21). The corresponding recursions are

36


– A G T T C A

0 1 2 3 4 5 6

– 0

A 1

C 2

C 3

G 4

T 5

T 6

0 0 0 0 0 0 0

0 1 0 -1 -1 -1 1

0 0 0 -1 -2 0 0

0 -1 -1 -1 -2 -1 -1

0 -1 0 -1 -2 -2 -2

0 -1 -1 1 0 -1 -2

0 -1 -2 0 2 1 0

ACCGTT---AGTTCA Fig. 2.20. Dynamic programming matrix for overlap alignment (top) and one of the corresponding three cooptimal alignments (bottom). The traceback path corresponding to the alignment is marked in bold. It starts from the maximum score in the bottom row or last column.

The traceback step starts from the maximum score in the entire dynamic programming matrix and proceeds until a cell with score zero is reached. As noted already for overlap alignments, there might be more than one possible starting point for a local alignment, and more than one cooptimal alignment may correspond to each starting point. Also, in the case of local alignments it is often interesting to include high-scoring but suboptimal alignments in the result set. These might correspond to weaker but still significant homologies.

2.9 Accommodating Affine Gap Costs So far we have simplified matters by using a linear gap model. However, as explained in Section 2.3, the affine gap scoring scheme is more realistic by distinguishing between gap opening and gap extension. The incorporation of the affine gap model in the dynamic programming algorithms seen so far follows from the insight that a gap insertion constitutes either the extension of an existing gap, or the opening of a new gap and its concomitant extension by one. To account for this, the three types denote the maximum of alignment endings need to be considered again. Let , the maximum score of an alignment score of an alignment ending in

2.9 Accommodating Affine Gap Costs

– A G T T C A

0 1 2 3 4 5 6

– 0

A 1

C 2

C 3

G 4

T 5

T 6

0 0 0 0 0 0 0

0 1 0 0 0 0 1

0 0 0 0 0 1 0

0 0 0 0 0 1 0

0 0 1 0 0 0 0

0 0 0 2 1 0 0

0 0 0 1 3 2 1

37

GTT GTT Fig. 2.21. Dynamic programming matrix for local alignment (top). The path corresponding to the optimal alignment (bottom) is marked in bold. In contrast to global and overlap alignments (Figs. 2.19 and 2.20), the local alignment consists of a pair of substrings from the input sequences.

ending in , and the maximum score of an alignment ending in . In order to calculate the recursions, we note not only , as we have done so and . Then the following recursions can be applied to the far, but also global alignment problem:

An example application of this strategy is shown in Figure 2.22 where the score scheme , , , and is applied to the two ATC and AC. The traceback starts in the bottom righthand sequences corner of the matrix with the highest entry there; in our case this is matrix with the entry -5. From there it moves diagonally, i.e. the nascent alignment consists of CC . At the same time, the traceback switches from matrix to matrix , which indicates

38


that a gap is opened and extended by one position in the sequence written vertically, yielding TC -C . The next move switches back to , thereby closing the gap, and adds one further position to return the final alignment, ATC A-C . The score of this is -5, as demanded by the affine gap model defined in Equation (2.1).

–

A

T

C

A

1

-10

-12

C

-10

-2

-5

–

0

–

–

A

T

0

-7

-9 -11

– 0

A

-6

A -7

C

-17 -9

–

C

-8

C -9

A

T

C

-6

-17

-19

ATC A-C Fig. 2.22. The three matrices needed for efficiently computing alignments with affine gap costs. Only the traceback for the final alignment (underneath the matrices) is shown (arrows). See text for further explanations.

Overlap and local alignment strategies can be adapted analogously for dealing with affine gap costs.

2.10 Maximizing vs. Minimizing Scores We noted in Section 2.2 that there are two ways of interpreting alignments. The first is that they minimize the implied number of evolutionary changes. Alternatively, one may say that they maximize the implied amount of sequence conservation. At a first glance these two interpretations appear to be equivalent. This begs the question, why all the scoring schemes we have covered in this chapter are of the maximizing kind? The reason for this is that local alignments depend on the cutoff score of zero, which implies as hidden assumption that the expected score of a random pair of residues is negative. If this condition were not met, the score of a local alignment would grow through the addition of random pairs of residues. With a minimizing scoring scheme no such natural numerical barrier exists. In order to make global alignment consistent with the local algorithm, global alignments are also usually calculated by maximizing a score even though in this case a minimizing scheme would work just as well because it always extends over the entire lengths of the input sequences.

2.12 Summary

39

2.11 Example Application of Global, Local, and Overlap Alignment Figure 2.23 shows the result of applying the global, local, and overlap alignment algorithms to the same pair of short DNA sequences. The global alignment looks reasonable, indicating that the sequences are indeed homologous across their entire lengths. The local alignment picks the block marked local in the global alignment. This is the highest scoring pair of substrings from the two sequences. In contrast to the global and local alignments, the overlap alignment has no biological meaning; there simply is no substantial overlap at the end of the sequences to be detected. Global, score = -52 local ACTTCACCAGCTCCCTGGCGGTAAGTTGAT---CAAA---GGAAACGCAAAGTTT GTTTCACTACTTCCTTTCGGGTAAGTAAATATATAAATATATAAAAATATAATTT TCAAG TCATC

49 55

54 60

Local, score = 7 GGTAAGT GGTAAGT

7 7

Overlap, score = 1 ACTTCACCAGCTCCCTGGCGGTAAGTTGATCAAAGGAAACGCAAAGTTTTCAAG-----------------------------------------------------GT

54 2

------------------------------------------------------TTCACTACTTCCTTTCGGGTAAGTAAATATATAAATATATAAAAATATAATTTTC

54 57

--ATC

54 60

Fig. 2.23. Optimal alignments computed using the three types of alignment algorithm discussed in this chapter. The input sequences consist of 60 nucleotides taken from the Adh locus in two species of Drosophila. : D. melanogaster; : D. adiastola. The scoring scheme was: match , mismatch , gap opening , gap extension .

2.12 Summary Alignments are used for sequence comparison and typically model two evolutionary events: point mutations and indels. A pairwise alignment between two sequences is generated by writing the two sequences on top of each other such that either two residues or a residue and a gap symbol are paired. An optimal alignment is one

40


that either minimizes the implied number of evolutionary changes or maximizes a particular scoring function. Residues are scored differently from indels. Match and mismatch scores are applied to nucleotides, while PAM and BLOSUM amino acid substitution matrices are used for protein sequences. Gaps are usually modeled using the affine score function, which distinguishes between gap opening and gap extension. Since the scores of alignments can be defined recursively, dynamic programming is used for efficiently calculating optimal alignments. There are three major types of such alignments, global, local, and overlap. A global alignment is based on the assumption that the sequences are homologous over their entire lengths. In contrast, local alignments search for local homologies. Finally, overlap alignments are used for the assembly of overlapping sequence fragments, as might be generated in the course of a shotgun sequencing project.

2.13 Further Reading An advanced treatment of alignment algorithms is given in the textbook by Waterman [253], who is also one of the coinventors of the optimal local alignment algorithm and a number of other algorithms in computational biology.

2.14 Exercises and Software Demonstrations 2.1. Consider the following DNA alignment: AACTCA---G ATCTGATTTG What is its score under the following scoring scheme: , ?

,

,

2.2. What is the probability of drawing a pair of distinct nucleotides from a nucleotide database under the assumption that all residues have the same frequency? 2.3. The genome of Mycoplasma genitalium has the following composition: Nucleotide Frequency A 0.346 0.158 C 0.159 G 0.337 T What is the probability of drawing a pair of distinct nucleotides from the genome of M. genitalium? 2.4. Consider the following protein alignment:

2.14 Exercises and Software Demonstrations

41

AAWGCCA AVWGCCL What is its score using the BLOSUM62 substitution matrix (Table 2.4)? 2.5. Use the bioinformer program Alignment Protein Substitution Matrices (Section A.1.1) to calculate the expected difference between a pair of protein sequences separated by 5 PAM and by 50 PAM. Do these values differ from the PAM number? Explain. 2.6. Imagine you are given the task of replacing the old system of match and mismatch scores for DNA sequence alignments by PAM matrices. From sequence alignments you have already computed the following mutation probability matrix for sequences that have diverged by one PAM. A C G T

A 0.97 0.01 0.01 0.01

C 0.01 0.97 0.01 0.01

G 0.01 0.01 0.97 0.01

T 0.01 0.01 0.01 0.97

In addition, the background frequencies of all nucleotides are

.

1. Compute the PAM1 substitution matrix. 2. Compute the PAM2 substitution matrix. 2.7. Use the information given in Figure 2.12 to compute the final BLOSUM score matrix for the clustered BLOCKS data. 2.8. Consider two random nucleotide sequences of equally likely nucleotides and a scoring scheme of , . What is the expected score of a random pair of residues? Can this scoring scheme be applied in the context of local alignment algorithms? 2.9. What is the number of global alignments between two sequences of lengths 3 and 4? 2.10. List all substrings of sequence

ACG.

2.11. What is the number of pairs of substrings that can be formed between ACG and ACGT? 2.12. What is the number of local alignments between

ACG and

ACGT?

2.13. Imagine a molecular biologist sends you a file containing the shotgun sequencing data for Escherichia coli strain K12. The genome of this bacterium consists of 4,639,675 nucleotides and the shotgun sequencing data consists of 82,500 fragments of an average length of 500 bp. 1. What is the coverage of this genome?

42


2. How many nucleotides do you expect to have remained unsequenced? 3. What coverage would have been necessary to obtain an expectation of a single unsequenced nucleotide? 2.14. Write a computer program for calculating the number of global alignments that can be formed between two sequences. How many different global alignments can be formed between two sequences of lengths 390 and 400? Compare your answer to that given by the bioinformer program Number of Alignments (Section A.1.2). 2.15. Write down the missing two cooptimal alignments implied by the dynamic programming matrix in Figure 2.20. 2.16. Construct an algorithm for computing the number of cooptimal global alignments. ATA and ATTA 2.17. Compute the optimal global alignment between , , using dynamic programming and a scoring scheme of , . What is the score of the optimal alignment? What is the optimal alignment? Is the optimal alignment unique? Compare your results to those of the bioinformer program Pairwise Alignment (Section A.1.3). 2.18. Global alignment can be regarded as a special case of local alignment. Rewrite the recursions for local alignment such that they also become applicable to the global case.

3 Biological Sequences and the Exact String Matching Problem One needs some form of memory with which any required entry can be reached at short notice. This difficulty presumably used to worry the Egyptians when their books were written on papyrus scrolls. It must have been slow work looking up references in them, and the present arrangement of written matter in books which can be opened at any point is greatly to be preferred. Alan M. Turing [117, p. 319]

The human genome consists of nucleotides [128, 252]. A printout of this text would generate a rather massive book, given that Shakespeare’s collected works characters—blanks, punctuations, and all. In other words, a printamount to out of the human genome would result in a small library of over 730 volumes, each the size of Shakespeare’s collected works. If we wanted to look up the usage of, say, the word “phoenix” in Shakespeare, we’d need to leaf through his oeuvre from start to finish. That is, such a search would take time proportional to the size of the bard’s works. However, if we had access to a concordance, i.e. an index, to the works of Shakespeare, we could find the occurrences of “phoenix” much more rapidly. In this case, our search would take time proportional to the length of the word or phrase we are looking for. This old but nevertheless very pleasing insight should not let us forget that the construction of a concordance is a painstaking business (the concordance to Shakespeare’s works by itself comprises nine volumes). Hence there is a trade-off between the effort of constructing a concordance and the total time saved through its usage. In this chapter we shall encounter computational methods that emulate both types of search mentioned so far: (i) linear in the size of the text and (ii) independent of the size of the text through indexing.

3.1 Exact vs. Inexact String Matching Biologists investigating a nucleotide or amino acid sequence often need to look up specific patterns in this sequence. A simple example would be to search for potential start codons in a protein-coding mRNA sequence. In this case we would be looking for all the positions where an exact match between a pattern (ATG) and a text (the mRNA sequence) starts.

44

3 Biological Sequences and the Exact String Matching Problem

Alternatively, many protein families are characterized by inexact matches to signature sequences. PROSITE is a data base dedicated to the collection of such signature sequences [226]. For example, mitogen-activated protein kinases (MAP kinases) form a class of threonine/serine kinases that are involved in many central cellular signal transduction pathways. They are characterized by the PROSITE-signature F-x(10)-R-E-x(72,86)-R-D-x-K-x(9)-C. Hyphens separate the elements of the pattern, letters refer to amino acids, and an x indicates any amino acid. Bracketed numbers denote the repeat length of a residue; if this repeat length varies, the range of this variation is quoted in the brackets. The pattern reproduced above might appear somewhat long-winded as it is spread out over approximately 100 residues. But it has the very useful property that all MAP kinases conform to it, while no other proteins in the comprehensive protein sequence database SwissProt [25] match this pattern. A PROSITE-signature is a compact description of a potentially large set of sequences. Strings that describe sets of strings through a special syntax are known as regular expressions and PROSITE-signatures are a particular kind of regular expression [80]. Notice that in order to generate such a signature, the approximate positions of conserved regions have to be known. These can only be discovered by comparing homologous proteins, i.e. proteins with a shared recent common ancestor. The detection of such homologous proteins is usually carried out with a different kind of inexact matching known as alignment. Alignments are dealt with in Chapters 2, 4, and 5. In this chapter we look at exact matching and matching with at most mismatches. We will see that exactly matching a pattern with a text can be achieved in time proportional to the length of the text, , using a variety of classical methods. In the first part of this chapter we introduce an example algorithm that achieves this time bound. Next we show how we can search for a set of patterns of total length rapidly in time, where is the number of occurrences of the patterns. Perhaps surprisingly, it is possible to improve dramatically on these strategies and to execute exact pattern searches in time proportional to the length of the pattern through the use of an index. We start our survey of exact string matching methods with a simple, “na¨ıve” approach to clarify the issues involved.

3.2 Na¨ıve Pattern Matching When comparing two characters, their identity is called a match, the converse a mis, match. We usually talk about two kinds of strings, the text, , with length , where and usually . Solving and the pattern, , with length the exact string matching problem then amounts to finding all occurrences of in . If we take as an example the DNA sequences CCACAGACACAT and ACA, then occurs three times in starting at positions 3, 7, and 9. A na¨ıve method for solving the exact matching problem is to align the left ends of and and to compare each character going from left to right. If a mismatch is found, or all letters in have been matched, is shifted to the right by one position

3.3 String Searching in Linear Time

45

Phase 1 Phase 2 Phase 3 Text CCACAGACACAT CCACAGACACAT CCACAGACACAT Pattern ACA ACA ACA ˆ ˆ *** Fig. 3.1. First three phases in na¨ıve algorithm for exact string matching. The algorithm proceeds from left to right; mismatches detected by the algorithm are indicated by a circumflex (ˆ), matches by a star ( ).

(Fig. 3.1). In the worst case and consist of a single type of character. Then the na¨ıve algorithm performs comparisons and its runtime is therefore . Through preprocessing of and (as we will explain in Section 3.3) the time, that exact matching problem can be solved much more effectively in is, in time linear in the size of the text.

3.3 String Searching in Linear Time There is a rich tradition of string matching algorithms in the computer science literature [99]. Here we describe one algorithm that runs in time linear in the length of the text, the Z-algorithm [99, p. 8ff]. The fundamental concept of the algorithm is that of a Z-value. Given a string and a string position , the Z-value is the length of the longest substring of that starts at and matches a prefix of . If we take as an example string the DNA CACAG, then , , , and so on. Notice that will sequence and is omitted. always be equal to As an extension of the concept of Z-values, the substring is called . In order to simplify our notation for a Z-box at every position where the algorithm, let and denote the righthand and lefthand borders of the Z-box corresponding to the current position in the text. , starts by comparing The algorithm to determine Z-values for string , with the prefix of until a mismatch is found. The length of the prefix of the matching substring is . If , we set to and to . Assume now for and the borders of the corresponding that we already know we use the Z-values calculated previously: Z-box, and . In order to compute 1. If , find new Z-box. If , and . , we define and and consider two cases: 2. If a) If , , and remain unchanged. , compare characters starting at positions and , until b) If , , and . a mismatch at position is found; then To solve the exact string matching problem, we consider the string where $ is a character that occurs neither in nor in . Every time occurrence of in has been found. Z-values can be computed in

$ , , an

46


and hence the exact string matching problem has been solved in time linear in the size of the text. Let us work through a concrete application of this algorithm. Our input string is CACAG, and we search for the pattern ACA. Figure 3.2 shows the again corresponding setup with the -values computed so far. We now wish to compute . Currently and ; since , we compute , as marked in Figure 3.2. Since , case 2(a) applies and and hence . index 1 2 34 5 67 8 9 9 0 10 0 3 ? String ACA$CACAG

Fig. 3.2. Z-algorithm: searching for ACA in CACAG.

The Z-algorithm deals with a single pattern at a time. However, it is also possible to simultaneously match an entire set of patterns against a text. The corresponding algorithms are based on trees, which are introduced next.

3.4 Trees The string search methods explained in the subsequent sections make use of a data structure that reappears in many parts of this book: a tree. Trees are used as index structures for rapid string searching in this chapter, as guide trees for multiple sequence alignment in Section 5.3, as phylogenies in Chapter 8, and as coalescent trees for simulating gene samples in Chapter 11 and again in Section 12.3. In fact, this book itself has a hierarchical, that is, tree structure. We follow the treatment of trees by Donald Knuth, who defines a tree recursively nodes containing one special node designated [151, p. 308]: A tree is a set of disjointed the root of the tree. All nodes except for the root are partitioned into , each forming a subtree of the root. Consider for example the sets, tree shown in Figure 3.3A. By convention the root is drawn at the top, so node is the root. It has three subtrees, , , and . These are connected to the root by edges, sometimes also called links or branches. The root of subtree is , and so on. The degree of a node is the number of subtrees rooted on it. Terminal nodes or leaves have degree 0, while branch nodes or internal nodes are leaves, the others are have degree 1. In Figure 3.3, nodes , , , and internal nodes. It is important to be able to refer to the relative positions of nodes in a tree. A root is called the parent of the roots of its subtrees, who are siblings and children of their parent. In Figure 3.3A, node is the parent of the siblings , , and . Each node in a tree can be assigned a level. The level of each node is one greater than the level of its parent and the level of the root is zero. Our example

3.4 Trees

47

trees in Figure 3.3 consist of three levels. In an ordered tree the order of subtrees matters, while in oriented trees it does not. If the two trees shown in Figure 3.3 were ordered, they would be different, while as oriented trees they are equivalent. In this book we shall imply that trees are oriented, unless stated otherwise. An important children. Conventional special kind of tree is the binary tree. In it each node has phylogenies such as the one shown in Figure 1.1 are a special case of binary trees, where all internal nodes have degree 2. As we will see in Chapter 8, there is a further peculiarity about phylogenetic trees: they can be rooted or unrooted (cf. Figure 8.2). This flies in the face of the mathematical definition just given, but corresponds to the usage biologists have established.

level 0 level 1 level 2

B

A

B

A

A

C

E

D F

D G

G

C F

B E

Fig. 3.3. Example trees with different subtree order.

Binary trees are important, because there is a simple method of representing trees as binary trees. Consider again our example tree Fiwith branch nodes of degree gure 3.3 and remove at each node all references to child nodes except for the first one. Next, insert references connecting groups of siblings, which leaves the total number of links in the tree unchanged. The result of this operation is shown in Figure 3.4A, which is a binary tree. This property becomes more apparent in the equivalent reprechanges the sentation Figure 3.4B, with the added rule, that traversal of the link level of the node, while traversal of the link does not. This brings us to tree traversal. Many algorithms on trees depend on the ability to efficiently visit each node of a tree once. Given a binary tree, tree traversal methods proceed by visiting the root, the left child, and the right child. Depending on the order in which the root is visited, we can distinguish preorder, inorder, and postorder traversal. Preorder traversal proceeds in three steps iterated for each node in the tree: 1. visit node, 2. traverse link to left child, 3. traverse link to right child. “Visiting” a node in this context means that the algorithm carries out some computation based on the node, while link traversal does no more than that. When applied to the root of the binary version of our example tree (Fig. 3.4B), preorder traversal visits the nodes in the sequence . Similarly, inorder traversal proceeds in the following three iterated steps: 1. traverse link to left child,

48

3 Biological Sequences and the Exact String Matching Problem A

B A B E

C

A B E

D

C

D

F

G

F G

Fig. 3.4. Representation of Figure 3.3A as binary tree. A: Generated by removing the child links except for the first one from each node and adding sibling links. B: Redrawn version of A.

2. visit node, 3. traverse link to right child. When applied to the root of Figure 3.4B, this procedure visits Postorder traversal, finally, proceeds thus:

.

1. traverse link to left child, 2. traverse link to right child, 3. visit node. This traversal of Figure 3.4B visits . When talking about trees, it is important to be able to visualize them and there are many ways to do this. The explicit style with nodes and edges chosen for Figure 3.3 will be encountered in several variations throughout this book. However, there is a simple representation using nested parentheses, which is often used for textual representations of trees. Transcribing Figure 3.3A in this notation yields . With these important preliminaries mastered, we are in a position to look at string matching with keyword trees.

3.5 Set Matching Using Keyword Trees Imagine you wish to establish the location of a set of PCR primers in the human genome. You could sequentially search the genome for every primer sequence. This , where is the total number of primers, i.e. patterns. Howwould take time ever, it is possible to make this multiplicative time behavior additive using a keyword tree.

3.5 Set Matching Using Keyword Trees

49

Keyword trees are used for matching a set of patterns against a text and this search method is also known as the Aho-Corasick-algorithm in honor of its inven, where ACG , tors [5]. Given a set of patterns, e.g. AC , ACT , and CGA , the corresponding keyword tree is constructed by starting with and a root node connected to a series of child nodes, (Fig. 3.5A). The leaf node where the each one labeled by a single character of first pattern terminates is labeled 1. The string obtained by concatenating the edge labels on the path from the root to a node is called that node’s path label. In our example, the path label of node 1 is ACG. Continuing with our tree construction we fit AC into the existing tree by matching its characters with the characters of the terminates at an internal node, and accordingly tree, starting at the root. Pattern ACT is added; it fits until the node 2 this node is labeled 2 (Fig. 3.5B). Next, and the remainder of is placed on a new branch. Hence, node 2 has now become CGA is added. Since its first a branching node (Fig. 3.5C). Finally, pattern character mismatches the label of the branch emerging from the root, a new branch is added to the root (Fig. 3.5D). This construction algorithm takes time proportional . to the combined lengths of the patterns, A

B

C

D A

A

A

A

C

1 ACG,

AC,

3 ACT,

Fig. 3.5. Construction of keyword tree for pattern set

1

A

1

T

T

1

G

C

2

G

2

G

C

G

G

C

C

2

3

4

CGA ACG AC ACT CGA .

The keyword tree shown in Figure 3.5 can be used for text searching in the following way: let be the starting position of a putative pattern in the text and the currently examined text position. Initially and are set to the starting position, usually 1. Start character comparisons at the root and match consecutive characters in the text along the branches of the keyword tree. By doing so, is increased at every step, while remains constant. If a labeled node is encountered, report an occurrence of the corresponding pattern starting at position . If a mismatch is found, increase by 1, set , and start again from the root. This “na¨ıve” algorithm leads to repeated examination of characters left of . In order to avoid this duplication of character comparisons, we introduce shortcuts in the keyword tree that enable us to stay below the root even after a mismatch has been encountered. These shortcuts are known as “failure links”. Consider node with path label . The failure link connects to that node in the tree whose path label is the longest suffix of . Figure 3.6 shows our example keyword tree with added

50


failure links. Any failure link from a node whose path label has length 1 must lead to the root. The failure links can be added to the keyword tree during a single breadth first traversal of the tree, leaving the time requirement of keyword tree construction unchanged.

A C

C G

2

G A

T

1

4

3 ACG,

Fig. 3.6. Keyword tree for pattern set shown as arrows.

AC,

ACT,

CGA

ACG AC ACT CGA including failure links

How do the failure links speed up the pattern search? Consider the text ACGA. When comparing this to our example keyword tree, we first encounter a hit to pattern 2 and then to pattern 1, before we cannot extend our match any further. In the na¨ıve algorithm we returned to the root at this point and resumed our pattern matching at position 2 in the text. In this case we would examine characters C and G twice. Instead, we follow the failure link and find a match to pattern 4 with one more character comparison (Fig. 3.6). Careful implementation of this speedup leads to a pat, where is the number of pattern occurrences [99, tern search in time p. 60]. If we include keyword tree construction, set matching therefore takes time . In many string matching applications the text is not only much longer than the combined pattern lengths, but it is also constant, while the patterns are variable. An example would be searching PCR primer sequences, which are typically between 15 to and 20 nucleotides long, in a bacterial genome consisting of the order of nucleotides. In this situation it becomes very interesting to have a string matching method that allows searches in time proportional to the length of the pattern, instead of the text. Turning the speed requirement of string searching on its head by making it independent of the size of the text may sound too good to be true. In fact, we started this chapter by describing a familiar method of achieving this for books: the book’s index. Suffix trees are a data structure that achieve the same as well as a number of more advanced string searching tasks.

3.6 Suffix Trees

51

3.6 Suffix Trees A suffix tree is a special kind of tree associated with a string , where . has as many leaves as the string has characters and these leaves are labeled . Each edge in the tree is labeled with a substring of . The defining feature of the suffix tree is that by concatenating the strings from the root along the edges leading is obtained. Figure 3.7 shows a suffix tree for ACCG. to leaf , the suffix If you start reading at its root and follow the path leading to leaf 2, you obtain the string CCG. This is the suffix that starts at position 2 in .

C

G

AC

G CG

CG 1

2

4 3

1 2 3 4 ACCG Fig. 3.7. Suffix tree of

ACCG.

In order to search for a pattern in , start by comparing the first character in to the first character of the edge labels emerging from the root of . If no match is found, we are done and know that occurs nowhere in . If, on the other hand, a match is found, follow the corresponding edge label comparing it to successive characters in . Every time a node is encountered, the correct edge for continuing the string comparison needs to be found. Once a pattern is detected by this process, we know that contains an exact match to . Moreover, the leaf labels in the subtree of the node in whose edge label the match ended indicate the position(s) of in . C and ACCG. If we start matching at the For example, consider pattern root of the suffix tree shown in Figure 3.7, we end at a node whose subtree contains leaves labeled 2 and 3. These are the start positions of in . Looking up the leaves of a subtree takes, to a first approximation, constant time. Given a suffix tree, the , because at most exact string matching problem is therefore solved in comparisons between characters in the pattern and characters in the text need to be performed. This time estimate is slightly simplified since at every node encountered during the search the number of necessary character comparisons is less or equal to the size of the alphabet over which is formed. Nevertheless, pattern matching in an existing suffix tree is extremely fast. This is highly advantageous when dealing with stable databases that are queried frequently, as is often the case in biology. Notice that it is possible that a suffix is a prefix of another suffix. For example, in ACGC the suffix C is a prefix of the suffix CGC. In this case, no suffix tree corresponding to exists, as the path labeled would not end at

52


a leaf. A simple method for guaranteeing the existence of a suffix tree is to ensure that the last letter of does not occur anywhere else in . In practice, a sentinel or terminator symbol, often denoted as $ is added to and is constructed from $ (Fig. 3.8).

AC

C GC $

5

$ GC$

GC

$

3

1

$

2

4

1 2 3 4 5 ACGC$ Fig. 3.8. Suffix tree of

ACGC including the terminator symbol $.

An extreme case of the identity of prefixes and suffices appears in suffix trees for strings consisting of a single type of character. An example of this is shown in Figure 3.9. A A

A

$ A$ 1

$

$

$

5 4

3 2 1 2 3 4 5 AAAA$

Fig. 3.9. Suffix tree of the uniform string

AAAA$.

Our examples of suffix trees were so far restricted to strings over the nucleotide alphabet. It should be clear that the definition of a suffix tree applies to strings over any alphabet and the suffix tree for the string MADAMIMADAM is shown in Figure 3.10. In suffix trees, internal nodes have at least two and at most as many child nodes as the size of the alphabet over which is constructed. In the case of DNA sequences A C G T $ . Hence, the maximal degree of the corresponding alphabet is . Figure 3.11 displays the suffix tree an internal node in a suffix tree is

3.6 Suffix Trees

IM AD

M

DA

A

AM

M

53

$

$ 3

12

9

7

1

10

11

$ IMAD AM$

$ IMAD AM$

4

$

M$

$ IMAD AM$

8

ADAM IM

AD A

M

M

DA $ IMAD AM$

2

5

6

1 2 3 4 5 6 7 8 9 10 11 12 MADAMIMAD A M $ Fig. 3.10. Suffix tree of

MADAMIMADAM$.

corresponding to the DNA sequence ACCGCTCAC, which contains an example of an internal node with the maximal degree. Before we explain the uses of suffix trees further, we take a brief look at their construction.

AC

$

1

C

8

TCTCA AC C$ $

$ AC TC AC$ TC GC CGCTCAC$ A C $ $

CAC$ CGCT $

10

GC

9

7

2

3

4

5

1 2 3 4 5 6 7 8 9 10 ACCGCTCAC $ Fig. 3.11. Suffix tree of

ACCGCTCAC$.

6

54


3.7 Suffix Tree Construction Suffix trees are usually constructed through a series of intermediate trees. One possible na¨ıve algorithm proceeds as follows: Construct a root and a leaf node labeled . The edge connecting these two nodes is labeled . This initial tree is con, verted to the final structure by successively fitting the suffices into the intermediate trees. As demonstrated in Figure 3.12, each of these fitting steps proceeds by matching the suffix along the edge labels starting at the root until a mismatch is encountered. Remember that due to the sentinel character a mismatch is guaranteed to occur. Once this is found, there are two possibilities: 1. The match ends at the last letter of an edge label leading to node . In this case, an edge labeled with the mismatched part of the suffix is attached to . This edge leads to a leaf labeled with the start position of the current suffix. Examples of this are steps A B and C D in Figure 3.12. 2. The match ends inside an edge label. In this case, an interior node is inserted just behind the end of the match. In addition, a leaf labeled is attached to via an edge labeled with the mismatched part of the suffix. An example of this splitting of an edge is step B C in Figure 3.12.

B

CG C

D

CG 1 3

1 2 3 4 ACCG

G

G CG

AC

G CG

CG

2

C

C

1

2

AC

AC

CG 1

CCG

1

AC

A

2

4 3

Fig. 3.12. Na¨ıve construction of suffix tree of ACCG. Starting at the first position and moving leftwards, the suffices of are successively fitted into the tree.

This na¨ıve algorithm runs in time, which makes it impractical for realistic applications. Fortunately, there are three principal algorithms available that run in linear time [177, 249, 259]. In other words, it is feasible to construct suffix trees with the same time behavior as the Z-algorithm introduced in Section 3.3. However,

3.8 Suffix Arrays

55

we have already seen that once such a suffix tree is available, it can be searched repeatedly in time proportional to the length of the pattern. In spite of the availability of linear-time construction algorithms, suffix trees come at some cost in both time and space. Since computer memory is often more limiting than computer time, we now take a brief look at the space issue.

3.8 Suffix Arrays A suffix tree has a maximum of nodes; its space requirement is therefore linear in the length of the text and we might be tempted to leave it at that. However, suffix tree nodes take up at least ten times more computer memory than a simple character [158]. Moreover, for efficient string searching the complete suffix tree usually needs to be held in the random access memory (RAM) of a computer. A more space-efficient representation of some of the information contained in a suffix tree is captured in a suffix array [172]. This consists of the lexical ordering of a string’s suffices. Such an ordering can easily be obtained from a suffix tree if edges are lexically ordered. For example, the edges of the suffix tree in Figure 3.11 are lexically ordered from left to right. A depth-first traversal of this tree retrieves the suffices depicted in Table 3.1 in lexical order. Since a bare-bones suffix array consists solely of a one-dimensional array holding the second column of Table 3.1, it is very space efficient. In practice, suffix arrays are reported to take up three to five times less space than suffix trees [172]. Table 3.1. Lexical ordering of the suffices contained in ACCGCTCAC. This can be obtained by noting the leaf labels during an inorder traversal of the suffix tree depicted in Figure 3.11. A suffix array simply consists of the suffix start positions uncovered in this fashion. index suffix start position suffix 1 8 AC$ 2 1 ACCGCTCAC$ 3 9 C$ 4 7 CAC$ 5 2 CCGCTCAC$ 6 3 CGCTCAC$ 7 5 CTCAC$ 8 4 GCTCAC$ 9 6 TCAC$

How can a suffix array be used for string matching? Since the array is ordered, it can be searched using a technique known as binary search. Such a search starts at the midpoint of the array and determines whether the corresponding suffix’s lexical

56


position is greater or less than that of the string concerned. In this way half of the array can be discarded for further search and this halving of the search space is repeated successively until a match is found. In the case of suffix arrays, the position of the highest and of the lowest match in the array should be searched for. These boundaries then delineate all positions at which the desired pattern occurs. If for ACCGCTCAC pattern C occurs, we example, we wish to know where in find the lowest matching position at index 3 and the highest matching position at index 7. The corresponding positions in the string are 9, 7, 2, 3, and 5. The utility of suffix trees extends beyond rapid string matching. One area of particular interest is their application to the detection of repetitive sequences, which are an important feature of the genomes of complex organisms.

3.9 Repetitive Sequences in Genomics—the

-value Paradox

The amount of DNA found in the haploid genome of an organism is called its value. Given that a genome encodes the organism of its host, it is perhaps surprising that there is little correlation between the -value and an organism’s complexity: The size of the genome of the unicellular baker’s yeast Saccharomyces cerevisiae is base pairs (bp) [88], which is roughly 200 times smaller than that of humans bp) [128], whose genome in turn is approximately 200 times smaller that ( that of the unicellular amoeba Amoeba dubia ( bp) [168, p. 383]. This lack of correlation between complexity and genome size in eucaryotes is an observation first made in the late 1960s [27] and is known to biologists as the -value paradox [201]. The -value paradox is mainly caused by the varying amounts of repetitive DNA found in metazoan genomes. Repetitive sequences can be classified into five categories [128, p. 879ff]: 1. transposon-derived repeats; these are also referred to as interspersed repeats and make up the vast bulk of repetitive DNA (Table 3.2); they vary in length between 100 and 6,000 bp and in the majority of cases are replicated through an RNA intermediate; 2. inactive retroposed copies of cellular genes, also referred to as processed pseudogenes; 3. simple sequence repeats, e.g. A or GC , also known as microsatellites; 4. segmental duplications, which are 10 kb-300 kb chunks of DNA copied from one region of the genome to another; 5. blocks of tandemly repeated sequences, e.g. centromeres, telomeres, and ribosomal gene clusters. The function of repetitive DNA remains unclear [201]. However, these elements make assembly of shotgun fragments (cf. Section 2.7) difficult, which is illustrated in Figure 3.13. This is a particularly tricky issue in the automated assembly of eucaryotic genomes [180]. On the other hand, repetitive sequences have come to play an important role in a number of molecular techniques. For example, microsatellites are highly variable and for this reason are used as markers in mapping and population

3.10 Detection of Repeated and Unique Substrings Using Suffix Trees

57

Table 3.2. Interspersed repeats as percentage of the genome of various multicellular eucaryotes [128, 40]. organism Homo sapiens Mus musculus Arabidopsis thaliana Caenorhabditis elegans Drosophila melanogaster

5’

trivial name interspersed repeats human 44.4% mouse 38.6% thale cress 10.5% nematode worm 6.5% fruit fly 3.1%

3’

Fig. 3.13. Automatic genome assembly is made difficult by repetitive sequence elements shown as boxes along a chromosome (line). In our example the 3’ end of fragment can either overlap with fragment or with fragment . Joining fragments and based on their overlap results in the correct assembly. In contrast, joining fragments and leads to the deletion of the genomic region .

studies. Further, the precise distribution of the most prevalent interspersed repeat in humans, the Alu element, forms the basis of DNA fingerprinting used in forensics. Thus, dealing with repetitive sequences is very much a feature of modern molecular biology. These repeats are usually inexact. They are routinely detected through aligning known repeat elements to the genome of interest. However, such inexact repeat elements often lead to an increased prevalence of exact repeats. In the following section we give a first introduction to suffix-tree-based algorithms for detecting exactly repeated as well as unique substrings.

3.10 Detection of Repeated and Unique Substrings Using Suffix Trees We have already seen in Section 3.6 that suffix trees can be used for rapid string matching. A corollary of the way in which this matching is carried out is that any prefix of an internal node’s path label is a repeat substring. The suffix tree depicted in Figure 3.11 has two internal nodes with path labels AC and C. These substrings together with the prefix A of AC are the only three repeats contained in the underlying text ACCGCTCAC: AC and A are both repeated twice (positions 8 and 1), while C is repeated five times (positions 9, 7, 2, 3, and 5).

58


We define the string depth of an internal node as the length of its path label. For example the node with string depth 5 in Figure 3.14 has path label MADAM, which consists of five characters. String depths can be assigned to internal nodes in a single depth first traversal. By looking up the internal node with, say, the greatest string depth, we can rapidly locate the longest exact repeat in a string. Moreover, we have already learned that, apart from the single root, trees only contain two kinds of nodes: internal nodes and leaves. Hence a path label either ends at an internal node, in which case it is a repeat substring, or at a leaf, in which case it is a unique substring.

IM AD

M

DA

A

AM

M

$

$

1

$

10

12

5

5

$ IMAD AM$

4

ADAM IM

M$

2

$ IMAD AM$

8

AD A

M

2

M

DA $ IMAD AM$

4

3

6

$ IMAD AM$

1

1

11

3

9

7

1 2 3 4 5 6 7 8 9 10 11 12 MADAMIMAD A M $ Fig. 3.14. Suffix tree of

MADAMIMADAM$ with string depths marked as circled numbers.

Unique sequence motifs are perhaps as important as repeat motifs in molecular biology. They play a role in the construction of PCR primers, the molecular identification of organisms, and the generation of specific antibodies. In natural sequences the number of unique sequence motifs tends to be of the same order of magnitude for a sequence of as the total number of substrings, i.e. length . As grows, the number of candidate unique sequences quickly becomes unmanageable. Here suffix trees suggest a way of reducing a problem of quadratic complexity to its linear version: Visit each leaf in a suffix tree and look up its parent’s string depth. This number plus one is the minimal length of a unique string starting such shortest unique substrings from which at the leaf’s position. There are all the remaining unique substrings can be generated by simple extension. Consider for example the suffix tree shown in Figure 3.14 and say that the first leaf we visit is 2. The string depth of its parent node is 4 and hence we know that the shortest . This is ADAMI and unique substring starting at position 2 has length

3.12 Generalized Suffix Tree

59

any extension of this string to the righthand side such as ADAMIM, ADAMIMA, etc. is unique.

3.11 Maximal Repeats As with unique substrings, there are usually very many repeat motifs contained in a given sequence, for instance, about 1/4 of all positions in the genome of Escherichia coli are occupied by A. In practice we are therefore often most interested in finding maximal repeats. These have the property that they become unique when extended either to the left or to the right. For example, the string TACACT contains a maximal repeat of length 1 (T) and a maximal repeat of length 2 (AC). In contrast, the substring A is a repeat, but not a maximal repeat. The central idea for the detection of maximal repeats is to look up the left neighbors of the leaf positions in the subtree of some internal node . For example, the ACCGCTCAACCGCTCA is shown in Figure 3.15. Its rightmost suffix tree for internal node has string positions 6 and 14 in its subtree. The left neighbor of both C. If in contrast to this example the left neighbors in the positions is subtree rooted on are occupied by more than one character, is designated leftdivergent [99]. The property of left-divergence propagates up the suffix tree, i.e. a node with a child node that is left-divergent is itself left-divergent. The path label of any left-divergent node corresponds to a maximal repeat. The path label of the leftdivergent node with the greatest string depth is the longest maximal repeat in . In our example suffix tree (Fig. 3.15) all left-divergent nodes are circled and you should verify that the longest maximal repeat in is ACCGCTCA, which is the path label of the left-divergent node with the greatest string depth. It occurs in at positions 1 and 9.

3.12 Generalized Suffix Tree So far, we have constructed suffix trees just from a single string. However, it is straight forward to generate what is known as a generalized suffix tree representand . We can proceed by ing more than one string. Consider the two strings and then inserting into this every suffix of . first constructing the suffix tree for All we need to do is change the leaf labels such that they now consist of pairs of numbers identifying both the string of origin and the position within that string. As ACCGCTCAC; the corresponding suffix tree is shown in an example, consider GCA, we obtain the tree topology Figure 3.11. If we insert into this suffix tree shown in Figure 3.16. Using the new labeling scheme for leaves, generalized suffix trees of an arbitrary number of strings can be constructed. This opens the way for solving the longest common substring problem.

60


A

TC

A

$

ACCG

ACCG

$ CTCA

$ CTCA

$

4

$

$

16

17

12

6

14

C

$

7

TC

$ CTCA

13

ACCG

$

5

8

$ CTCA

$

11

A

ACCG

$ CTCA

CG

CT

CA

$

$

3

A

ACCG

$ CTCA

$ CTCA

10

CCGCTCA A C CG CT CA $

CA

TCA

GCT

ACCG

ACCG

2

GC

15

1

9

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 ACCGCTCAA C C G C T C A $ Fig. 3.15. Suffix tree for ACCGCTCAACCGCTCA$. The circled nodes are left-divergent (see text for details). The left-divergent node with the greatest string depth is labeled by the longest maximal repeat in : ACCGCTCA.

3.13 Longest Common Substring Problem The longest common substring problem is motivated by elementary biological considerations. When comparing two or more coding DNA sequences, regions that are well conserved are often of particular biological interest. They might encode functionally important domains of the corresponding protein or point to novel regulatory elements. In addition, they represent suitable sites for PCR-primers that work across all of the sequences considered. So there are many occasions when we wish to know the longest substring shared by all members of a set of strings. For example, the two ACCGCTCAC and GCA are GC and longest common substrings between CA. To find the longest common substring between our example sequences ACCGCTCAC and GCA, we need to find the lowest common ancestor of leaves and a suffix from . One way to do this is to note for containing a suffix from each internal node the string identifier of the leaves in its subtree. Those nodes that and . are marked 1 and 2 end at a path that is labeled by a substring common to All we need to do now is search for the internal node marked by 1 and 2 that has the greatest string depth. In Figure 3.16 these are the two boxed nodes.

3.14

-Mismatches

The examples presented so far have given a first impression of the usefulness of suffix trees in the context of exact string matching. However, in biology we are often more

3.14

A

C

$

1,3

1,6

$

$

$ 1,1

1,2

AC

TCAC A$

$ AC TC GC CAC$ CGCT A

1,8

GC TCAC $

TC

1,9

61

1,5

2,1

1,4

C$ $

CAC$ CGCT $

2,3

C

$

1 , 10 2,4

-Mismatches

2,2

1,7

1 2 3 4 5 6 7 8 9 10 ACCGCTCAC $ GCA$ Fig. 3.16. Generalized suffix tree for the strings ACCGCTCAC$ (cf. Figure 3.11) and GCA$. The leaf labels consist of pairs of numbers , where denotes the string of origin, and the suffix position within that string. Notice that leaves may carry as many labels as there are input strings. The path labels of the two boxed nodes are the longest common substrings between and .

interested in similar rather than identical sequences. It turns out that the concept of the lowest common ancestor first introduced in the context of searching for longest common substrings (Section 3.13) is also useful for extending the application of suffix trees to inexact matching. A standard version of the inexact matching problem is known as the -mismatch problem. Given a string , the task is to find all positions of pattern with at most mismatches. Now, the string depth of any lowest common ancestor for two leaves pointing to positions and in indicates the length of the longest match starting in positions and . The same observation applies if we build the generalized suffix tree for and ; in this case the string depth of a lowest indicates the length of common ancestor for position in and position in and . So we start by looking for the lowest common the match starting at ancestor between leaves labeled and . If its string depth, , is equal to , . On the other hand, if , we have found an exact match of at position repeat the procedure by looking for the lowest common ancestor for leaves pointing and . If at most repetitions of this step are sufficient to to cover , a match has been found starting at position . Lowest ancestor queries can be carried out in constant time [99, ch. 8]. Therefore, generalized suffix trees allow . In other words, by allowing only the detection of -mismatches in time

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a single mismatch, the runtime of our string search changes from being proportional to the length of the pattern to being proportional to the generally much greater length of the text. ACCGCTCAC and In order to use this procedure to find repeats between GCA with at most mismatch, we can return to the generalized suffix tree and ; the corresponding depicted in Figure 3.16. We start at the leaves labeled lowest common ancestor is the root, whose string depth is zero. So the next step is and . The string depth of the lowest common to look at the leaves labeled is mismatched. Since and are misancestor is 1, which means that at . This procedure matched, we conclude that there is no 1-mismatch of and is is repeated until the lowest common ancestor of the leaves labeled visited (boxed node in Figure 3.16). Its string depth is 2 and, hence, we have found a at . Another 1-mismatch occurrence of starts 1-mismatch occurrence of . at Notice that the inexact matching we have carried out so far does not encompass insertions and deletions (indels). However, these are an integral feature of sequence evolution and need to be accounted for in many biological contexts. Indels cannot be handled easily using suffix trees. As explained in Chapter 2, string comparisons incorporating indels are best done using alignments based on dynamic programming. We shall see in Chapter 4 that a combination of dynamic programming and exact matching techniques leads to very fast and memory-efficient alignment algorithms.

3.15 Summary In computational biology one often needs to look up the occurrence of some pattern in a text . Since the texts of computational biology include genome sequences, which tend to be large, it is important to apply efficient methods of string matching. , where is Traditional string matching methods are guaranteed to take time the length of the text. By preprocessing a set of patterns into a keyword tree, this time requirement can be extended to set matching. Instead of preprocessing one or more patterns, it is also possible to preprocess the text. A suffix tree is a data structure that . However, once it is constructed, it can can be constructed for a given text in be used to search any in in time , where is the length of the pattern. In addition to making string searching extremely efficient, a suffix tree reveals in one fell-swoop the entire repeat structure of without the need for carrying out any string comparisons. This has important biological applications where unique and repeat sequences play a central role in many fundamental as well as biotechnological problems. Finally, suffix trees can also be used for rapid inexact string matching, mismatches between and its occurrence in are allowed. where

3.16 Further Reading Gusfield has written a comprehensive and detailed guide to string algorithms in computational biology [99].


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3.17 Exercises and Software Demonstrations 3.1. Suffix tree construction is relatively time consuming while searching a suffix tree is quick. Use the program Match String Matching in the bioinformer software (Section A.2.1) to see how this assertion compares with timings of realworld string searches. 3.2. Generalized suffix trees provide a data structure for efficiently finding longest exact matches between two sequences. Construct an algorithm based on dynamic programming for achieving the same end. Compare the run times of the two approaches. AAGCG and compare your result with 3.3. Draw the suffix tree corresponding to that calculated using the program Match Suffix Tree of the bioinformer software (Section A.2.2). 3.4. Suffix trees are ideal for determining exact repeats in strings. The program Match Repeat Search in the bioinformer software (Section A.2.3) implements a search for repeat sequences based on a suffix tree. Use this program to find the longest repeat sequence in the first genome to be completely sequenced— that of bacteriophage X174 [214]—as well as the longest repeat sequence in the paper in which James Watson and Francis Crick first proposed the doublehelical structure of DNA [256].

sdfsdf

4 Fast Alignment: Genome Comparison and Database Searching In the process of routinely screening new sequences having no known relatedness to previously determined protein sequences, we noted regions of similarity of the catalytic chain of bovine cAMP-dependent protein kinase (BOV-PK) to the inferred amino acid sequences of the src gene products (transforming proteins) of Moloney murine sarcoma virus (MMSV) and of Rous avian sarcoma virus, Schmidt-Ruppin strain (RSV-SR). Winona C. Barker and Margaret O. Dayhoff [16, p. 2836]

Biology has traditionally been a comparative science and computational molecular biology has inherited this approach in the form of a strong emphasis on sequence comparison. If genomic regions of closely related organisms are available, a full alignment of these is conceptually a simple extension of the global alignments of genes that were a staple of the gene-centered pre-genomic era of molecular biology. However, pairwise alignment by dynamic programming as introduced in Chapter 2 , where and denote the lengths of has a space and time requirement of the sequences compared. This does not scale well. In order to tackle the memory requirement of optimal alignment algorithms, observe that in order to fill the corresponding dynamic programming matrix, the algorithms first encountered in Section 2.6 need only save one row (or column) plus one additional cell. Starting from such a configuration (Fig. 4.1A), the algorithm stores the value of a new cell and erases a cell that is not needed in subsequent steps (Fig. 4.1B). This is repeated until the last cell in the row has been filled (Fig. 4.1C) and the circle can start again (Fig. 4.1D). Since the dynamic programming matrix can be filled either column-wise or row-wise, the maximum score of an alignment can thus be calculated in space proportional to the shorter of the two sequences being aligned. However, in most cases we are interested in obtaining the actual alignment rather than simply its score. Based on the idea of computing the score in linear space, an elegant algorithm has been devised that returns the alignment in linear space, while leaving the time requirement proportional to the size of the original dynamic programming matrix [116, 181]. This is a remarkable advance, as the memory requirement of an algorithm may pose an insurmountable barrier, whereas a scientist

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4 Fast Alignment: Genome Comparison and Database Searching

should never run out of patience. Still, with exponentially growing databases speed is important. A

B

C

D

Fig. 4.1. A–D: Filling in the dynamic programming matrix using the space corresponding to just one row plus one cell. Solid cells are active, dotted cells have been deleted.

For improving the run time of global alignment algorithms (as opposed to their memory requirement), a more radical departure from dynamic programming is necessary. One possible approach developed for aligning genomes of closely related bacteria is discussed in this chapter. It involves a combination of suffix trees introduced in Chapter 3 and dynamic programming introduced in Chapter 2. On the other hand, initial annotation through homology involves finding a local inexact match between a short query sequence and a large number of subject sequences that make up the text of the search. In this case we are looking for one or more local alignments between the query sequence and the database entries. These databases may be very large. For instance, the GenBank sequence repository has been growing exponentially since it was started in 1982 (Fig. 4.2). Searching GenBank by dynamic programming is as impractical as in the case of globally aligning bacterial genomes. However, the task is also of a different kind, as we expect that the vast majority of entries in the database are of no interest in our search. Hence most contemporary algorithms for searching biological sequence data bases aim to filter or exclude these irrelevant regions before committing to a more detailed (and time-demanding) investigation of regions that might contain a match. In addition to algorithmic problems, database searching also poses statistical challenges. When finding an alignment between a query sequence and an entry in a potentially very large database, it is important to know how likely this match would occur by chance alone. In this chapter we first give an example of doing biology by fast global genome comparison before explaining the algorithmic issues involved. This is followed by two examples of using rapid local alignments as a guide to biological function, which leads into an explanation of the corresponding computational ideas. Finally, an application of fast local alignment to the problem of discovering the extent of genomewide gene duplication motivates our treatment of the statistical issues involved in database searching.

4.1 Global Alignment

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1e+10 1e+09

Count

1e+08 1e+07 1e+06 1e+05 10000 1000 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Year

Fig. 4.2. The GenBank sequence repository has grown exponentially since its inception in 1982. : residues (nucleotides and amino acids); : entries. Data taken from

www.genome.jp/dbget/db growth.html.

4.1 Global Alignment Fast global alignment is often used for comparing the genomes of closely related organisms. This is particularly interesting in organisms where the relationship between genotype and phenotype is comparatively easy to investigate such as in bacteria. Many pathogenic bacteria have been subjected to comparative genome sequencing. For instance, Vibrio parahaemolyticus is a gram-negative marine bacterium and a frequent cause of food poisoning among people fond of seafood. A pandemic of gastroenteritis caused by this organism prompted the sequencing of its genome in 2003 [171]. In order to learn more about its virulence mechanism, the resultant sequence of its two chromosomes, one 1.8 Mb and the other 3.3 Mb in length, was compared to the genome sequence of the related bacterium Vibrio cholerae. The latter causes cholera, a more severe epidemic diarrhea. Like V. parahamolyticus, the genome of V. cholerae is organized in two chromosomes. One of the tools for carrying out the genome comparison was the software MUMmer [48], which rapidly computed a

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global alignment of the larger of the two chromosomes [171]. The researchers were surprised to find distinct virulence mechanisms in this comparison, even though both bacteria cause similar diseases. V. parahaemolyticus contains a complex of genes known as the type III secretion pathway, which is common among diarrhea-causing pathogens including shigella, salmonella, and enteropathogenic Escherichia coli. V. cholerae, on the other hand, does not contain the type III secretion pathway. Such comparative information on bacterial pathogenicity obtained by genome alignment is likely to impact future treatment of the corresponding diseases [171]. How does MUMmer achieve efficient global alignment on a genomic scale? Strategy for Fast Global Alignment In order to make direct pairwise comparisons between complete genomes feasible, MUMmer is based on a generalized suffix tree [48]. Recall from Section 3.12 that a suffix tree for a single sequence can be generalized to index any number of sequences. Here, we are interested in comparing only two sequences (genomes). In the corresponding generalized suffix tree exact matches between the two sequences are marked by internal nodes whose subtrees contain entries from both sequences. MUMmer carries out three steps to align whole genomes [48]: 1. identify all maximal unique matches (MUMs) between the two genomes; such repeats cannot be extended and occur only as a single pair of matching substrings between and not within the genomes; 2. find the longest increasing subsequence of MUMs; 3. process the gaps in the resulting alignment. We now explain these steps further. Notice first of all that—apart from the uniqueness criterion—MUMs correspond to the maximal repeats introduced in Section 3.10. These can be found in a suffix tree in linear time. The intuition behind searching for these maximal repeats is that they are highly likely to form part of the final global alignment. , where is the start To fix our ideas, let us represent a MUM by the triplet position in genome , the start position in genome , and the length of the repeat. We can sort the triplets representing these fragments according to their position in, say, genome . This induces a labeling on the matched fragments in genome , which is not necessarily monotonously increasing (Fig. 4.3, top panel). The next step in the algorithm is to find the longest increasing subsequence of MUMs from genome . This may well lead to the loss of fragment pairs. For example, in the bottom panel of Figure 4.3 fragment 4 has been removed from the set of MUMs in both genomes. The longest increasing subsequence of MUMs usually contains gaps which need to be filled next. Depending on the type of gap, their processing varies: large insertions are simply marked graphically in the final alignment, while polymorphic regions are subjected to alignment by dynamic programming. Since the two genomes that are being compared are assumed to be closely related, the time spent on gap closure by dynamic programming should be negligible [48].

4.2 Local Alignment 2

1

3

5

4

69

Genome

Genome

1

2

2

1

4

3

5

5

3

Genome

Genome

1

2

3

5

Fig. 4.3. Sorting step in the algorithm for constructing global pairwise alignments of whole genomes. Top panel: Maximal unique matches (MUMs) sorted according to position in genome . MUM 4 has been transposed. Bottom panel: Longest increasing subsequence extracted from the MUMs of genome . Notice that in our example the longest increasing subsequence is not unique, would also be a valid subsequence. The configuration of MUMs induced by the longest increasing subsequence is finally subjected to gap processing. (Redrawn from [48].)

Recall that alignment by standard dynamic programming is based on a rather simple evolutionary model which encompasses only mutations and short indels. Genomes, however, may be subject to large-scale changes due to, for example, transposition or horizontal gene transfer. The approach to global alignment presented above can account for such changes. In addition, it runs in time linear in the combined length of the input sequences if these are closely related [48]. Fast global alignment algorithms are set to play an increasing role as genome data from closely related organisms accumulates [110]. However, annotation of individual genes contained in these genomes is usually carried out using methods for rapidly detecting local alignments.

4.2 Local Alignment Rapid local alignment methods are often applied in the context of searching biological sequence databases. One of the earliest examples of how such a database search can illuminate a protein’s function is connected to a seminal discovery in medicine: the pathogenicity of cancer-causing viruses is due to their expression of genes derived from homologous genes expressed in healthy cells. This astonishing relationship between virology, cancer, and normal cell proliferation has its origins in work started early in the last century. In 1911, the American Virologist Peyton Rous described in chicken the first infectious agent capable of causing cancer. Fifty-five years later Rous was awarded the Nobel Prize for his discovery of the “Rous Sarcoma Virus” (RSV) as it had become known by then. The genome of RSV consists

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of a single-stranded RNA molecule comprising 9,392 nucleotides and by the time of Rous’ Nobel Prize in 1966 it was speculated that this RNA molecule was reverse transcribed into a DNA molecule, which then integrates into the genome of its host [251, 23]. In 1970, David Baltimore and Howard Temin published independently the discovery of an enzyme that could affect this reverse flow of genetic information: the reverse transcriptase. RSV thus became the archetypical retrovirus, a group of morphologically similar viruses that also contains the human immunodeficiency virus (HIV). In the year after the discovery of reverse transcription, Peter Vogt isolated a mutant that had lost the ability to transform its host cells into neoplastic cells, but that replicated normally. This transformation-deficient yet replication-competent mutant of RSV could be compared by hybridization to its wildtype, leading to the isolation of the sequence-fragment that caused the difference between the two. This fragment was then used as a probe specific for the gene, which became known as src. The protein product of this gene was isolated in 1977 and its activity as a tyrosine protein kinase was established by 1980. Protein kinases play a central role in intracellular signaling, which explains how a single transforming gene can cause the many changes involved in the transition from normal cell cycle to neoplastic growth. Intriguingly, a protein indistinguishable from the src protein was soon discovered in healthy, uninfected cells. It became evident that the virus had captured a cellular gene sometime in the evolutionary past. This gene had remained so well conserved that it could be detected by successfully annealing a src-specific DNA probe from chicken to the genomes of other vertebrates and even members of other phyla, including Drosophila and Hydra [251, 23]. Still, the cellular identity of the src gene remained elusive. In 1981 the protein sequence of a bovine tyrosine kinase was published. This was compared to the 1,560 other protein sequences known at the time, revealing that it was similar to src. The immediate conclusion from this computerized database search was that the sequenced mammalian tyrosine kinase was a proto-oncogene, i.e. a gene that occurs in normal cells but may cause cancer upon changes in its expression level, or due to certain point mutations. Unfortunately, the normal function of this gene remained unknown at the time, although the authors speculated that it might play a central role in normal growth and development [16]. The feat of assigning a function to a proto-oncogene through mere database searching was achieved two years later by Russell Doolittle and his colleagues [50]. They showed that the transforming protein of another tumor retrovirus, the simian sarcoma virus, was homologous to the sequence of human platelet-derived growth factor (PDGF). This protein is stored in platelets and released into the serum in response to wounding. PDGF contributes to wound healing by stimulating cell division and it is not difficult to imagine how constitutive over-stimulation of cell division might lead to neoplastic growth. Today, database searches for annotating sequences form a routine part of research in molecular biology. It turns out that the various programs available for this task are based on the common algorithmic idea of basing slow, inexact string matching on rapid, exact matching.

4.2 Local Alignment

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4.2.1 Global/Local Alignment: -Error Matching In Section 3.14 we first mentioned the -mismatch problem, which is solved by finding all occurrences of pattern (length ) in text (length ), such that an occurrence has at most mismatches. We now return to this problem and expand its biological applicability by not only allowing mismatches but also indels. Let denote the maximum number of errors (mismatches or indels) allowed in a match. The crucial observation for the design of fast alignment programs is that an occurrence of in implies that there exists at least one substring of with a in . The length of can easily be computed by dividing perfect match of length into contiguous substrings of equal length: (4.1) Figure 4.4 illustrates this idea for . If we divide the query into segments of equal length and randomly distribute the errors along the query, at least one of the fragments remains error free [15].

Fig. 4.4. An occurrence of pattern , a perfectly matching substring of length

, with at most .

errors, implies that

contains

The important insight here is that an exact match of non-trivial length is a necessary condition for an inexact match. The resulting algorithm is divided into two phases, a search and a checking phase. At the beginning of the search phase the patcontiguous segments of equal length. is then searched tern is divided into for perfect matches to these segments. One rapid way of doing this would be to use a times. As a more memory-efficient alternative, suffix tree of and search this substrings of might be preprocessed into a keyword tree (cf. Section 3.5). the For a random pattern and text with equiprobable characters, the expected number , is equal to the probability of finding a of matches found in the search phase, match of length times the length of the text, times the number of starting points for fragments of length :

where is the size of the alphabet. In the checking phase the matches returned by the search phase are processed and , the algorithm examines further. If a match is found starting at positions

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substring for a match using dynamic programming. , while the length of is only Figure 4.5 illustrates this reasoning. Here and hence given in units of . The algorithm is designed for the case where bp. The choice of the substring of to be aligned we can think of as, say,

Fig. 4.5. The principle of -error matching. The bold segment of length starting at position in the pattern forms an exact match with the bold segment at position in the text . A substring of the text spanning the length of the pattern plus the maximal error at each margin is aligned with the pattern. The traceback starts at the cell with the maximum entry out of the rightmost cells and stops upon reaching the top row. In case errors are encountered along the path, the traceback is aborted. Not drawn to scale.

with means that there are dynamic programming operations for each exact match found during the search phase. Therefore, the algorithm runs in time proportional to In other words, the algorithm runs in time . This might look like a dis. appointing result given that straight dynamic programming takes time However, as long as the number of exact matches of length is small, the algorithm is fast. Notice two things about -error matching. (i) Its result is a type of alignment we have not seen so far, as the query is aligned globally, while its match to the subject is local. Colloquially we call this a glocal alignment. (ii) For biological sequences the value of compatible with homology is usually unknown. In spite of this, the strategy of MUMmer is clearly analogous. The central idea is to exclude “unpromising” regions from dynamic programming through the use of exact matching. This

4.2 Local Alignment

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exclusion strategy also forms the basis of two of the most popular search tools for biological databases. 4.2.2 Examples of Database Search Programs Currently, perhaps the most frequently applied database search programs are FASTA and BLAST. Both are based on the exclusion idea. FASTA was published in 1988 and quickly became widely used by molecular biologists [197]. BLAST was published two years later as an attempt to improve on the performance of FASTA [9]. Both programs have evolved considerably since their inception more than a decade ago. Here we restrict ourselves to outlining central algorithmic ideas incorporated in the original versions. FASTA A common method for visualizing similarities between two sequences is known as a dotplot. Two sequences are written along the two dimensions of a grid and matches are marked by dots. Figure 4.6 shows a dotplot comparing the string madamimadam with itself. As a consequence of the self-comparison, all cells on the diagonal from

Fig. 4.6. Dotplot comparing the string madamimadam with itself. Each pair of identical characters is marked by a dot.

the top left to the bottom right contain a dot. Moreover, madamimadam is a palindrome and hence the opposite diagonal going from the bottom left to the top right

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cell is also dotted 1 . In addition, the off-diagonal dots indicate the fact that madam is repeated as well as palindromic itself. Dotplots of DNA sequences are usually heavily populated with dots. It is therefore customary to restrict them to runs of matches of a certain minimal length, which then appear as diagonals. An example of this is shown in Figure 4.7, where the sequences of the mRNAs of human -globin and -globin are compared. Figure 4.7C suggests that the central portions of both sequences are more highly homologous than the approximately 100 bp at their 5’ and 200 bp at their 3’ ends. The central idea for dramatically speeding up local alignment compared to straight dynamic programming is to concentrate on diagonals with the highest score in order to exclude the other areas of the matrix from explicit searching [253, p. 172]. Notice that in the context of FASTA the dotplot is used only metaphorically to get an intuitive understanding of the algorithm; it is not a data structure actually built during execution of the algorithm. Instead, the central data structure used by FASTA is known as a hash table. This is a generalization of an array. An array consists of entries that are indexed by monotonously increasing integers usually starting at or . A hash table is an array that is indexed by arbitrary objects such as strings. This hash index is converted to an ordinary array index using a hash function [41, ch. 11]. The great advantage of such a data structure is that searching for one of the indices, also referred to as keys, is very quick. The hash table we are interested in has as keys words of a certain length and as entries their positions in the text. For example, if we hash the text ACCAGAGAATT into words of length , we obtain Table 4.1. Table 4.1. Text

ACCAGAGAATT hashed into words of length 2. key position AC 1 CC 2 CA 3 AG 4, 6 GA 5, 7 AA 8 AT 9 TT 10

FASTA proceeds in four steps: . This hash 1. The query sequence (pattern) is hashed into words of length in the table is then used to localize exact matches of substrings of length Notice that in molecular biology, as opposed to natural languages, a palindrome is a sequence that is equal to its reverse complement, for example ACGT.

4.2 Local Alignment A

75

B

C

Fig. 4.7. Dotplot of the mRNA sequences of human -globin ( ) and -globin ( ). A: Matches of length ; B: matches of length ; C: matches of length . Notice the increasing sparseness of the plots, as well as the fact that, in contrast to the dotplot in Figure 4.6, the vertical sequence runs from bottom to top.

subject sequence (text). These matches can be imagined as diagonals of length in a dotplot. 2. The ten diagonals with the highest density of matches are rescored using a scoring matrix (in the case of protein sequences) or a simple DNA scoring scheme. 3. The diagonal regions that after rescoring have a score above some threshold are joined into one region. 4. The joined region is aligned by dynamic programming. In order to gain some insight into the time requirement of this algorithm, let us concentrate on the filtering of exact matches from step 2 onwards. This takes time proportional to the number of dots in the dotplot [253, p. 176]. For random sequences of lengths and this amounts to

which is essentially . This might strike the reader as a rather disappointing result, as it is identical to the time requirement of standard dynamic programming.

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However, the probability of finding a match of length , , is usually very small, thereby still speeding up execution considerably. Consider, for instance, . In this case an alphabet of equiprobable nucleotides and , amounting to a potential time saving of orders of magnitude [197]. As in the MUMmer program introduced in Section 4.1, FASTA proceeds by applying dynamic programming only to a very small portion of the space of possible alignments. The next logical step would be to try doing without dynamic programming altogether. This was also the conclusion of the developers of the Basic Local Alignment Search Tool described next. BLAST BLAST was published two years after FASTA and at the time was an order of magnitude faster [9, 18]. Like FASTA, BLAST is based on the exclusion of unpromising areas of the alignment matrix from detailed searching. The central notion of the BLAST algorithm is the maximal segment pair (MSP). This is a pair of segments (substrings) of identical length whose score cannot be improved by extension on either side. Figure 4.8 shows the three steps of BLAST: (i) compilation of a list of highscoring words, (ii) a scan of the subject sequences contained in the database for exact matches to these words, and (iii) extension of the exact matches. We cover each step in turn. Construction of a list of high-scoring words (Fig. 4.8A) is essentially a refine-word approach described in the context of FASTA. Given a protein ment of the query sequence, all substrings (words) of a certain length that have a match with a score somewhere in the query are stored along with their match positions in the query. Table 4.2 shows the word list constructed from the example query AHYV . This word list contains 15 entries, based on the BLOSUM62 score matrix and which is a considerable expansion of the three exact matches that can be formed (AH, HY, and YV). In practice, a word length of 3 is frequently used for protein sequences. was found to give satIn conjunction with the PAM120 substitution matrix, isfactory results [9]. The list of words [179] resulting from the choice of and contains approximately 50 entries for each residue in the query, such that a protein sequence of length 100 would induce a word list comprising perhaps 5,000 entries [9]. The preprocessing of a DNA query sequence is simpler than that of proteins just described. Given a DNA query, it is hashed into words of length , which is typically set to 12. The scanning phase (Fig. 4.8B) poses an exact matching problem that should by now look familiar to the reader. If the database is stable, its transformation into the suffix tree format would allow a rapid retrieval of all positions of word matches. However, the authors of BLAST chose the far more memory-efficient option of preprocessing the distinct query words into a keyword tree. The scan phase therefore

4.2 Local Alignment

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Query

A

B Subject

C

Subject MSP

MSP

Fig. 4.8. The three steps in the BLAST algorithm. A: Generate list of high-scoring words from query; B: search for exact matches between members of the word list and subject; C: generate maximal segment pairs (MSPs) by extending exact matches to left and right as long as the score increases. Notice that MSP could only be extended to the left.

takes time, where is the size of the database, the length of the word list, and the number of matches of members of the word list. Finally, in the extension phase (Fig. 4.8C) matches returned from the scanning phase are extended in either direction. This continues until the score of the MSP remains below a previous maximum score for, say, 20 residues. The extension phase takes time proportional to the number of matches returned by the scanning phase. Under a random sequence model this is proportional to the probability of finding a , multiplied by the size of the database and the certain word of length , . If we model our database as consisting length of the word list: [9]. of equiprobable nucleotides, this time requirement becomes Run time assessments are associated with a classical trade-off between sensitivity and specificity encountered in many database search programs. Sensitivity is the proportion of true homologues returned by a search. Consider, for example, a data-

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Table 4.2. List of high-scoring words constructed from the query sequence AHYV using word length , score threshold , and the BLOSUM62 substitution matrix (Table 2.4). Member of Word List Match in Query Score AH AH 12 CH AH 8 GH AH 8 SH AH 9 TH AH 8 VH AH 8 HY NY YY HF HW

HY HY HY HY HY

15 8 9 11 10

YV YI YL YM

YV YV YV YV

11 10 8 8

base containing 20 MAP kinases. A search for MAP kinases returns 25 sequences of which 18 are MAP kinases. In this case the sensitivity of the search program is . This quantity can trivially be maximized by returning the entire database. In contrast, specificity is the portion of true homologues among the set of hits returned. . Again, this can The specificity of our example search for MAP kinases is be maximized in a trivial fashion by returning very few high-scoring hits. An ideal . database search program is characterized by specificity sensitivity In the case of BLAST, the algorithm runs in time proportional to the sum of the time taken by each of the three steps outlined above: [9]. The larger the word list (i.e. ), the smaller the probability that a significant MSP will be missed, i.e. the higher the sensitivity. On the down side, this leads to a longer run time and possibly a decrease in specificity. For a given query, the size of the word list is determined by the word length, , and the threshold score, . Increasing inflates the word list, but this effect is offset by the reduced likelihood of finding the longer word in the database. This leaves as the critical parameter determining speed and sensitivity: Lowering increases the size of the word list thereby making the program more sensitive and also slower. However, there appear to be diminishing returns to in conjunction with a PAM120 score lowering , and in test runs a value of matrix recovered virtually all MSPs from various protein superfamilies [9].

4.5 Application: Determining Gene Families

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4.3 Database Composition In our discussion of the -error string matching algorithm as well as of FASTA and BLAST we have repeatedly referred to a random sequence model. This simplified our time analyses, but biological sequences are highly non-random due to locally biased base composition (e.g. A/T-rich regions) and repetitive elements. A successful database search tool needs to take account of this fact, since a query sequence containing a region that matches a repeat element might otherwise return a huge number of spurious results. For this reason BLAST incorporates two somewhat ad hoc but effective filtering steps: The first scans the frequency of all 8-mer words in the database and ignores matches to words with grossly elevated frequencies. The second scans a compact library of repetitive elements and ignores query words that return a match to one of its members.

4.4 Heuristic vs. Optimal Alignment Methods All three of the fast alignment programs mentioned in this chapter, MUMmer, FASTA, and BLAST, are based on so-called heuristic algorithms. In contrast, the alignment algorithms based exclusively on dynamic programming are known as optimal methods as they guarantee to yield the best alignment under the scoring scheme. In the context of MUMmer the heuristic nature of the algorithm means that there might be alignments of the input sequences that have a higher score than the alignment returned by the program. Similarly, for heuristic local alignment programs, there is word a (usually small) probability that a significant match does not contain a or a member of the word list employed by BLAST and the corresponding homology is missed. However, extensive tests have shown that in practice this problem is negligible compared to the huge saving in time and memory achieved by the three programs. Database searching raises not only algorithmic issues but also the statistical problem of deciding whether a given local alignment might be due to chance alone. Perhaps the most important innovation of BLAST was to incorporate statistical theory allowing the direct computation of the significance of an MSP [134].

4.5 Application: Determining Gene Families Many real-world applications of database search programs are critically dependent on an assessment of the significance of a given alignment. We introduce such an application in the context of the investigation of gene families in complex genomes. In many eucaryotic organisms the deletion of a gene (knockout) has no phenotypic effect. On the other hand, the genome projects of the past decade have confirmed that in eucaryotes many genes exist as members of gene families. In fact, the probability of a gene being duplicated has been estimated to be as high as 1% per million years, which is greater than the probability that the gene mutates in this

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time window [170]. This high incidence of gene duplication suggests that the lack of phenotypic effects in knockouts might in many cases be due to compensation by a duplicated gene. In order to investigate this idea further, Li and coworkers took advantage of two extraordinary data sets: (i) the complete genome sequence of brewer’s yeast, Saccharomyces cerevisiae, which contains 6,357 genes and (ii) a set of measurements on the fitness effects of knockouts for 5,766 of these genes [93]. The first step in the analysis was to distinguish genes that were not duplicated (“singletons”) from duplicated ones. This was done by an all-against-all FASTA search of the 5,766 protein sequences of the genes included in the study. Singletons were defined as genes that had no significant homology to any other gene in the genome, while duplicates of their had at least one significant hit and in addition could be aligned over sequence with their putative homologue. Figure 4.9 illustrates a procedure for extracting gene families from these pairwise comparisons. The results of the all-against-all comparison of a proteome just . A match beoutlined can be summarized in a pairwise match matrix is denoted by and if no homology was tween protein and detected or if . The protein families are now assembled by starting from the first entry equal to 1 in the matrix and recursively visiting the next 1 in that column followed by the next 1 in that row. Every time a 1 is encountered, the corresponding protein is added to the current cluster and the matrix entry is set to zero. This are zero. An analogous but more sophisprocedure is repeated until all entries in ticated clustering strategy is implemented in the program blastclust, which is distributed as part of the BLAST software package. Proteome

Match Matrix 0 1 0 0 0

1 0 0 1 0

0 0 0 0 1

0 1 0 0 0

Protein Families 0 0 1 0 0

,

Fig. 4.9. Strategy for the detection of protein families within a proteome.

Such a procedure led to the identification of 1,275 singletons and 1,147 gene families in the yeast proteome. In the subsequent comparison between the singleton/duplicate classification and the data set on fitness effects of knockouts, duplicated genes had a significantly higher probability of functional compensation than singletons. Overall approximately 1/4 of deletions with no phenotypic effect could be accounted for by gene duplication [93].

4.6 Statistics of Local Alignments

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4.6 Statistics of Local Alignments In Section 4.4 the notion of a significant homology was central to the classification of genes into families. The direct calculation of this significance was one of the major advances in the development of fast local alignment tools. 4.6.1 Maximum Local Alignment Scores The distribution of extreme values taken from multiple samples, be it the maximum height of people in various towns, the maximum length of proteins in a sample of proteomes, or the maximum score of local alignments between random sequences, are described by the extreme value distribution. Its probability density function is given by (4.2) where is the location parameter and the dispersion parameter. Figure 4.10 shows and . The cumulative distribution this probability density function for function of the extreme value distribution is (4.3) which is also shown in Figure 4.10 for

and

.

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 -2

0

2

4

6

Fig. 4.10. Probability density function, , and cumulative distribution function, the extreme value distribution with parameters and .

8

, of

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In order to estimate the parameter for the distribution of maximum local alignment scores, let be the background frequency of the -th residue in the database, e.g. the amino acid background frequencies displayed in Figure 2.7. Then is the unique positive solution of

where is the log-odds score of the residue pair taken from a given substitution matrix, e.g. BLOSUM62 (Table 2.4) [134]. The parameter is estimated as (4.4) where and are the effective lengths of the query and subject sequences, and is a scaling parameter. The effective lengths of query and subject are less than their raw lengths, which is due to the fact that high-scoring local alignments cannot start at the ends of sequences. This edge effect, that is the difference between effective and actual lengths, tends to become negligible for sequences longer than 200 residues. , entries in an alignment which lies between 0 and 1, corrects for the fact that the matrix are not independent, as the paths to which these scores refer may intersect. is more involved than and the interested reader is referred to Computation of a publication by Karlin and Alschul [134]. By substituting into the complement of Equation (4.3), we obtain the probability of observing a score greater or equal to some threshold score by chance alone: (4.5) As an example application of the statistics of local alignments, we compare human -globin with a lupine leghemoglobin, which consist of 146 and 153 amino acids, respectively. Using the BLOSUM62 substitution matrix, a gap opening score of -12 and a gap extension score of -1, the optimal alignment is shown in Figure 4.11 and has a score of 39. Is this score significant? human -globin:

50 TPDAVMGNPKVKAHGKKV 67 | . ||...|| || lupine leghemoglobin: 50 TSEVPQNNPELQAHAGKV 67 Fig. 4.11. Optimal local alignment between human -globin and lupine leghemoglobin.

According to the BLAST output, the effective length of -globin is 130, that of , and . Hence we can compute leghemoglobin is 137, . By substituting these estimates for and into . Equation (4.3) and taking the complement, we get Figure 4.12 shows the distribution of 100,000 maximum local alignment scores obtained by shuffling -globin and realigning it to leghemoglobin. This can be comand pared to the expected distribution obtained by substituting

4.6 Statistics of Local Alignments

83

into the probability density function given by Equation (4.2). The simulated probby chance alone is 0.024, which is very close ability of obtaining a score of to the result of 0.022 obtained much more efficiently by applying the extreme value distribution. We can conclude that the alignment found is marginally significant.

0.12 simulated theoretical

0.1 0.08 observed score

0.06 0.04 0.02 0 15

20

25

30

35

40

45

50

55

60

65

70

Fig. 4.12. Simulated and theoretical distribution of optimal local alignments scores. The simulated distribution was obtained by shuffling human -globin 100,000 times and aligning it to lupine leghemoglobin.

Instead of the probability of getting a score greater or equal to that observed, , some implementations of BLAST quote the expected number of alignments with a score of at least . This is known as the -value, which is calculated by assuming that a given dynamic programming matrix contains only few entries with . The distribution of rare events can often be modeled by the Poisson a score distribution. We start by considering the probability of an event occurring at least once, which is simply the complement of the event not occurring at all. In the conis text of our discussion, the probability of finding an alignment with score . For the complement of finding no significant alignment: Poisson-distributed events , where is the mean of the distribution. , and we solve Here

for

to obtain

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Hence we get the expectation value by taking the logarithm of the complement of : Equation (4.5) and multiplying by (4.6) It is important to remember the distinction between the expectation value attached to a score and its significance. The range of expectation values ( ) is bounded , while probabilities lie between zero and one. However, for small by zero and , the -value and the corresponding -value are very probabilities, say similar as shown in Figure 4.13.

4 3.5 3 2.5 2 1.5 1 0.5 0 20

25

30

35

40

45

Fig. 4.13. The number of random alignments expected to have a score , , and the probability of obtaining a score , , as a function of the score, . The parameters for this example are taken from the alignment of human -globin and lupine leghemoglobin described in the text, in which case and .

4.6.2 Choosing a Substitution Matrix We explained in Section 2.10 that the mean score of a random pair of residues should be negative for local alignments. However, the difference in score between a random and a statistically significant match is maximized if the entries in the scoring matrix fulfill a further requirement. Let be the frequency of amino acid in the database be the probability with which amino acids and are found in homoloand let gous positions in protein alignments. Then the scoring scheme that best distinguishes

4.8 Summary

85

significant from spurious alignments contains entries for residues and of the form [8]

This is the log-odds form of the entries found in both the PAM and the BLOSUM matrices (cf. Section 2.4). Moreover, entries of this format are on average negative, i.e. Of the two quantities that go into calculating an entry in a scoring matrix, is the more interesting. It represents the frequencies of homologous pairs of residues. This set of “target frequencies” changes over evolutionary time, hence the series of scoring matrices. By choosing one of these, the user essentially chooses the set of target frequencies she judges appropriate for the alignment problem in hand. Notice that if evolution has gone on for long enough as to completely randomize residues . In other words, in the limit of infinite time in homologous positions, the target frequencies become equal to the corresponding product of the background frequencies and all log-odds are zero.

4.7 Bit Scores So far we have considered “raw scores”. These have the disadvantage of being dependent on and , i.e. on parameters that are functions of the score matrix employed and the background frequencies of the residues in the database. This makes it difficult to compare the significance of alignments obtained using different score matrices. In order to obtain expectation values that are independent of the score matrix, a bit score has been defined: (4.7) Substituting Equation (4.7) into Equation (4.6) we get (4.8) The corresponding significance is obtained by the Poisson approximation already outlined:

4.8 Summary The comparison of DNA and protein sequences is one of the central applications of bioinformatics in biological research. As the amount of data available for meaningful comparisons has grown exponentially since the early 1980s, rapid algorithms for approximating alignments obtained by optimal methods have become central to

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the practice of sequence analysis. The methods for rapid global and local alignment introduced in this chapter are all based on the idea of excluding large parts of the sequences compared from detailed investigation. In the global approach, the aim is to cover as much of the alignment as possible through exact matches between the two sequences analyzed. These exact matches can be discovered efficiently by the application of a generalized suffix tree. In the local approach, promising regions in the database are identified through an exact match to a substring of the query. This procedure is heuristic as there is a (small) probability that a homologous sequence might not contain an exact match to the substrings of the query. Designing the set of query substrings is therefore a critical step in database search programs such as FASTA and BLAST. Since these programs often scan large databases, it is important to gain a measure of statistical significance for a given alignment. The statistical theory accounting for the significance of local alignments is formally restricted to ungapped alignments, but appears to work equally well for gapped alignments [198]. The significance of an alignment is summarized by the expectation value shown in Equation (4.8). This corresponds to the number of alignments with a score greater or equal to the one observed that are expected to occur by chance alone.

4.9 Further Reading Bedell and his colleagues have written a comprehensive guide to the BLAST software package [18] .

4.10 Exercises and Software Demonstrations 4.1. Consider a set of sequences containing 40 hexokinases. You compare a known hexokinase to your collection using a pairwise alignment tool and obtain 35 hits, of which 28 are true hexokinases. What is the sensitivity and specificity of your search program? 4.2. Consider the following sequence of numbers

and use dynamic programming to find its longest increasing subsequence. 4.3. The genome of E. coli K12 has the following composition: A 1,142,228 bp C 1,179,554 bp G 1,176,923 bp T 1,140,970 bp 1. What is the probability of drawing two identical nucleotides from this genome? 2. What is the expected length of the longest repeat sequence?


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3. Let

be an E. coli query sequence of length 100 nucleotides. In a search with errors, what is the length of the substring of that is guaranteed to be error-free? 4. How many exact matches of this length are expected to occur in the E. coli genome? 4.4. Hash the string AGTAAC into substrings of length 2 and compare your result to that obtained using the program Match Hash Table in the bioinformer software (Section A.2.4). 4.5. Familiarize yourself with the idea of a dotplot by working through the tutorial for the bioinformer program Match Dotplot in Section A.2.5). 4.6. How would you construct an alternative solution to Exercise 3.2 that runs in time linear in the sum of the lengths of an arbitrary number of input strings? 4.7. You are given an unknown human protein and compare it to the mouse proteome using BLAST. An abridged version of the BLAST output is shown in Figure 4.14. 1. What is the function of the unknown protein? 2. Is EWL part of the query sequence’s word neighborhood? 3. Calculate the raw score of the local alignment between the query and the sushirepeat containing protein. 4. Convert the raw score just computed to a bit score and compare your result to the corresponding bit score on the printout. 5. Is the homology to the sushi-repeat containing protein reported in Figure 4.14 significant? 6. You repeat the BLAST search a year later using the same database, whose size has doubled in the meantime. Everything else being equal, what result do you expect for a) the score? b) the -value?

4.8. Given the BLOSUM62 score matrix, the raw score of the optimal local alignment between human -globin and lupine leghemoglobin shown in Figure 4.11 is 39 and its bit score is 19.6. 1. Recalculate the raw score based on the BLOSUM45 matrix shown in Table 4.3. 2. Convert the raw score to a bit score using the parameters and returned by BLAST for the alignment of -globin and leghemoglobin under the BLOSUM45 substitution matrix.

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4 Fast Alignment: Genome Comparison and Database Searching Sequences producing significant alignments: gi|NP_062728.1| neuronal protein; neuronal protein N... gi|NP_848713.1| transgelin 2 [Mus musculus] gi|NP_081114.1| sushi-repeat containing protein [Mu...

Score E (bits) Value 408 308 27

e-114 2e-84 9.9

>gi|NP_062728.1| neuronal protein; neuronal protein Np25 [Mus musculus] Length = 199 Score = 408 bits (1048), Expect = e-114 Identities = 198/199 (99%), Positives = 198/199 (99%) Query: 1 Sbjct: 1

MANRGPSYGLSREVQEKIEQKYDADLENKLVDWIILQCAEDIEHPPPGRAHFQKWLMDGT 60 MANRGPSYGLSREVQEKIEQKYDADLENKLVDWIILQCAEDIEHPPPGRAHFQKWLMDGT MANRGPSYGLSREVQEKIEQKYDADLENKLVDWIILQCAEDIEHPPPGRAHFQKWLMDGT 60

>gi|NP_848713.1| transgelin 2 [Mus musculus] Length = 199 Score = 308 bits (790), Expect = 2e-84 Identities = 144/199 (72%), Positives = 170/199 (85%) Query: 1 Sbjct: 1

MANRGPSYGLSREVQEKIEQKYDADLENKLVDWIILQCAEDIEHPPPGRAHFQKWLMDGT 60 MANRGPSYGLSREVQ+KIE++YDADLE L+ WI QC ED+ P PGR +FQKWL DGT MANRGPSYGLSREVQQKIEKQYDADLEQILIQWITTQCREDVGQPQPGRENFQKWLKDGT 60

>gi|NP_081114.1| sushi-repeat containing protein [Mus musculus] Length = 467 Score = 27.3 bits (59), Expect = 9.9 Identities = 12/44 (27%), Positives = 22/44 (50%) Query: 226 HLLTFSSFSKPSVPGFCKCCISAENPRCLLLPPPVHLELCKDSA 269 H++ ++++ + CK + + RC +L PP H L SA Sbjct: 239 HVIRYTAYDRAYNRASCKFIVKVQVRRCPILKPPQHGYLTCSSA 282

Database: mouse_protein.fa Posted date: Dec 19, 2003 3:47 PM Number of letters in database: 11,582,853 Number of sequences in database: 25,371 Lambda 0.319

K H 0.136

0.413

Gapped Lambda 0.267

K H 0.0410

0.140

Matrix: BLOSUM62 Gap Penalties: Existence: 11, Extension: 1

Fig. 4.14. Abridged output of a BLAST search.


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Table 4.3. BLOSUM45 amino acid substitution matrix. Match scores are shown in bold. A R N D C Q E G H I L K M F P S T W Y V

A 5 -2 -1 -2 -1 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -2 -2 0

R -2 7 0 -1 -3 1 0 -2 0 -3 -2 3 -1 -2 -2 -1 -1 -2 -1 -2

N -1 0 6 2 -2 0 0 0 1 -2 -3 0 -2 -2 -2 1 0 -4 -2 -3

D -2 -1 2 7 -3 0 2 -1 0 -4 -3 0 -3 -4 -1 0 -1 -4 -2 -3

C -1 -3 -2 -3 12 -3 -3 -3 -3 -3 -2 -3 -2 -2 -4 -1 -1 -5 -3 -1

Q -1 1 0 0 -3 6 2 -2 1 -2 -2 1 0 -4 -1 0 -1 -2 -1 -3

E -1 0 0 2 -3 2 6 -2 0 -3 -2 1 -2 -3 0 0 -1 -3 -2 -3

G 0 -2 0 -1 -3 -2 -2 7 -2 -4 -3 -2 -2 -3 -2 0 -2 -2 -3 -3

H -2 0 1 0 -3 1 0 -2 10 -3 -2 -1 0 -2 -2 -1 -2 -3 2 -3

I L -1 -1 -3 -2 -2 -3 -4 -3 -3 -2 -2 -2 -3 -2 -4 -3 -3 -2 5 2 2 5 -3 -3 2 2 0 1 -2 -3 -2 -3 -1 -1 -2 -2 0 0 3 1

K -1 3 0 0 -3 1 1 -2 -1 -3 -3 5 -1 -3 -1 -1 -1 -2 -1 -2

M -1 -1 -2 -3 -2 0 -2 -2 0 2 2 -1 6 0 -2 -2 -1 -2 0 1

F P -2 -1 -2 -2 -2 -2 -4 -1 -2 -4 -4 -1 -3 0 -3 -2 -2 -2 0 -2 1 -3 -3 -1 0 -2 8 -3 -3 9 -2 -1 -1 -1 1 -3 3 -3 0 -3

S 1 -1 1 0 -1 0 0 0 -1 -2 -3 -1 -2 -2 -1 4 2 -4 -2 -1

T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -1 -1 2 5 -3 -1 0

W Y V -2 -2 0 -2 -1 -2 -4 -2 -3 -4 -2 -3 -5 -3 -1 -2 -1 -3 -3 -2 -3 -2 -3 -3 -3 2 -3 -2 0 3 -2 0 1 -2 -1 -2 -2 0 1 1 3 0 -3 -3 -3 -4 -2 -1 -3 -1 0 15 3 -3 3 8 -1 -3 -1 5

sdfsdf

5 Multiple Sequence Alignment The camel and the Llama are closely related species with different habitats. Camels live in the plains and Llamas high up in the Andes. The camel possesses a hemoglobin molecule with an affinity for oxygen that is normal for an animal of its size. But because of a single mutation in the gene coding for one of the two globin chains that make up a hemoglobin molecule, the Llama has a hemoglobin with an unusually high oxygen affinity. The variant hemoglobin helps the Llama breathe in the rarefied mountain air. Max Perutz [200, p. 204]

The motivation for carrying out pairwise alignment is often to infer function from sequence similarity. In the case of multiple alignments this motivation is reversed: Given that a group of protein sequences have similar functions, we would like to know which sequence features are essential for this function. Such essential sequence motifs should be conserved, i.e. be similar, between homologous sequences from diverse species. The easiest way to discover conservation is to align many sequences encoding the function of interest and to look for invariant regions. Figure 5.1 shows an alignment of seven globins, which are heme-containing proteins involved in oxygen storage or oxygen transport [49]. The alignment contains four globin sequences from mammals, one globin sequence from a fish, one myoglobin sequence from a mammal and finally a globin from a leguminous plant, a leghemoglobin. The last common ancestor of plants and animals existed over one and a half billion years ago. In spite of this, the sequences can be aligned in a meaningful way using the software clustalw [246], perhaps the best-known programs available for this purpose. How do we know that the alignment shown in Figure 5.1 is meaningful? One method is to compare it to the three-dimensional structure of one of the aligned proteins. Myoglobin from sperm whale (Fig. 5.1) was the very first protein whose structure was solved. This work was carried out by John Kendrew and published in 1960 [137], seven years after the structure of DNA had been determined in the same Cambridge laboratory [256]. Not only was Kendrew the first to solve the threedimensional structure of a protein, he was also a proto-bioinformatician, for he extensively used the first computers acquired by Cambridge University after the second World War to compute the structure of myoglobin from X-ray diffraction data [47, p. 111ff]. A refined version of the original myoglobin model is shown in Figure 5.2. The figure displays the eight helices that are also marked in the multiple alignment

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5 Multiple Sequence Alignment

human horse human horse lamprey g whale m lupine l consensus

--------VHLTPEEKSAVTALWGKVN--VDEVGGEALGRLLVVYPWTQR --------VQLSGEEKAAVLALWDKVN--EEEVGGEALGRLLVVYPWTQR ---------VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKT ---------VLSAADKTNVKAAWSKVGGHAGEYGAEALERMFLGFPTTKT PIVDTGSVAPLSAAEKTKIRSAWAPVYSTYETSGVDILVKFFTSTPAAQE ---------VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLE --------GALTESQAALVKSSWEEFNANIPKHTHRFFILVLEIAPAAKD !* ** * * ! ** * * *** * * ! *

40 40 41 41 50 41 42

Conserved Histidine human horse human horse lamprey g whale m lupine l consensus

FFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDN-----LKGTF FFDSFGDLSNPGAVMGNPKVKAHGKKVLHSFGEGVHHLDN-----LKGTF YFPHF-DLS-----HGSAQVKGHGKKVADALTNAVAHVDD-----MPNAL YFPHF-DLS-----HGSAQVKAHGKKVGDALTLAVGHLDD-----LPGAL FFPKFKGLTTADQLKKSADVRWHAERIINAVNDAVASMDDT--EKMSMKL KFDRFKHLKTEAEMKASEDLKKHGVTVLTALGAILKKKGH-----HEAEL LFSFLKGTSEVP--QNNPELQAHAGKVFKLVYEAAIQLQVTGVVVTDATL ! * *** ** ***!**** * ** *** *

85 85 80 80 98 86 90


ATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVA AALSELHCDKLHVDPENFRLLGNVLVVVLARHFGKDFTPELQASYQKVVA SALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLA SNLSDLHAHKLRVDPVNFKLLSHCLLSTLAVHLPNDFTPAVHASLDKFLS RDLSGKHAKSFQVDPQYFKVLAAVIADTVAAG---------DAGFEKLMS KPLAQSHATKHKIPIKYLEFISEAIIHVLHSRHPGDFGADAQGAMNKALE KNLGSVHVSKG-VADAHFPVVKEAILKTIKEVVGAKWSEELNSAWTIAYD !* *!* ** *** ** ** * *** * *** * *


GVANALAHKYH-----GVANALAHKYH-----SVSTVLTSKYR-----SVSTVLTSKYR-----MICILLRSAY------LFRKDIAAKYKELGYQG ELAIVIKKEMNDAA--* * **

135 135 130 130 139 136 139

146 146 141 141 149 153 153

Fig. 5.1. Alignment of seven globin sequences. Dark gray: identical amino acids; light gray: helices in myoglobin; / : / -globin; g: globin 5; m: myoglobin; l: leghemoglobin.

(Fig. 5.1). In the alignment we also notice that there are seven amino acids that have not changed over their entire evolutionary history exceeding one and a half billion years. Two of these conserved positions are specially marked histidines, which interact with the heme group of the globins. The heme group is responsible for the ability of globins to act as oxygen transporters and the amino acid residues that interact most intimately with this molecule are necessary for the proper functioning of all globins. The two histidines and the heme molecule of myoglobin are displayed in Figure 5.2. From the multiple alignment we can infer the histidines in the homologous proteins that interact with the heme molecule. Multiple alignments are ubiquitous in molecular biology. They are used to 1. find sequence motifs that characterize protein families; 2. discover homologies between an unknown sequence and known sequence families; 3. support the prediction of secondary and tertiary structure; 4. support the design of PCR primers;


93

H93

H64

Fig. 5.2. 3D-Structure of oxy-myoglobin. The two histidines interacting with the heme group are shown in atomic detail. Notice also the bound oxygen molecule.

5. calculate phylogenetic trees. In this chapter we will first look at the computation of multiple sequence alignments. We start by a natural extension of the dynamic programming algorithm explained in Section 2.6 and learn about a powerful speedup of this method. However, the speedup is not decisive enough to make dynamic programming a viable option when trying to align tens or hundreds of sequences, as one may wish to do when investigating all the members of a large protein family such as the globins [133]. In these cases heuristic multiple alignment methods developed since the 1980s are usually applied. In essence these methods reduce the multiple alignment problem to a series of pairwise alignments. Heuristic multiple sequence alignments make use of two data structures not mentioned so far: sequence profiles and phylogenies. Both are used to compute multiple sequence alignments; on the other hand, they constitute classical applications of multiple sequence alignments. We treat phylogeny reconstruction in Chapter 8 and profile analysis in Section 6.1.

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In the following discussion of the computation of multiple sequence alignments we concentrate on global approaches throughout, but local versions are of course also possible and have been implemented [229, 184].

5.1 Scoring Multiple Alignments Before we can compute a multiple alignment we need a scoring scheme. One of the most popular schemes for scoring multiple sequence alignments is the sum-of-pairs method. Here the score of a multiple sequence alignment, , is defined as

where

is the length of the alignment, the number of sequences, and is the score of position of sequence aligned with position of sequence . The alignment of two gap symbols is scored as zero.

5.2 Multiple Alignment by Dynamic Programming As explained in Section 2.6, dynamic programming based on a two-dimensional matrix can be used to efficiently compute the score of a pairwise alignment. For sequences the corresponding -dimensional matrix can be represented as an dimensional hyperlattice. Figure 5.3 displays the cuboid for aligning three sequences. The path through this cuboid corresponding to a particular alignment can be repre. The path depicted in Figure 5.3 sented as a series of coordinate triplets and ends at posicorresponds to a global alignment, as it starts at position tion , where , , and correspond to the lengths of the aligned sequences: . Notice that for each dimension the current coordinate indicates how many residues of the relevant sequence have been incorporated into the alignment up to that point. Now consider the alignment of globin sequences shown in Figure 5.1. If we , and write the hyperlattice conumber sequences from top to bottom ordinates in the same order, the path implied by the multiple sequence alignment is: . The number of vertexes in the hyperlattice is . This number . increases rapidly with and for our example alignment it is of the order of While the volume of our hyperlattice increases by a factor proportional to the length of each additional sequence, the time spent at each vertex also increases. This is because the number of neighboring vertexes that can influence the score at the current vertex increases, as illustrated in Figure 5.4. For two sequences we have already seen that the current score is formed as a function of three neighboring scores.

5.2 Multiple Alignment by Dynamic Programming

95

V DPQV AG−N A−−V

A N G

A D

P

Q

V

Fig. 5.3. Cuboid for the alignment of three sequences. The three sequences are written along the three axes of the hyperlattice. The path corresponding to the alignment shown on the right visits the circled vertexes.

For three sequences seven scores need to be considered, and for four sequences 15 scores, where is scores. In general, each new matrix entry is a function of the number of sequences to be aligned. So the total time needed to align sequences by “na¨ıve” dynamic programming is . current node current node

2 sequences: 3 vertices to visit


current node


Fig. 5.4. The number of vertexes that need to be visited by a “na¨ıve” optimal alignment algorithm at each vertex in the hyperlattice as a function of the number of sequences to be aligned.

96


We called the approach to multiple alignment as presented so far “na¨ıve” because the algorithm can be drastically accelerated by considering only those vertexes in the hyperlattice that potentially lie on the path of an optimal alignment [35]. If the sequences align well, this will be a narrow “tube” along the main diagonal of the hyperlattice. The program msa implements such an improved optimal alignment algorithm [169, 98]. Running the program on our data set of seven globins on a 1 GH pentium 4 computer with 250 MB RAM took 0.2 seconds. The resulting multiple alignment is shown in Figure 5.5 and is very similar to our original example obtained using a heuristic method (Fig. 5.1). However, there are four differences between the two alignments, which all occur around the three largest gaps in the alignments. human horse human horse lamprey g whale m lupine l consensus

--------VHLTPEEKSAVTALWGKV--NVDEVGGEALGRLLVVYPWTQR --------VQLSGEEKAAVLALWDKV--NEEEVGGEALGRLLVVYPWTQR ---------VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKT ---------VLSAADKTNVKAAWSKVGGHAGEYGAEALERMFLGFPTTKT PIVDTGSVAPLSAAEKTKIRSAWAPVYSTYETSGVDILVKFFTSTPAAQE ---------VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLE --------GALTESQAALVKSSWEEFNANIPKHTHRFFILVLEIAPAAKD !* ** * * ! ** * * *** * * ! *

40 40 41 41 50 41 42


FFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDN-----LKGTF FFDSFGDLSNPGAVMGNPKVKAHGKKVLHSFGEGVHHLDN-----LKGTF YFPHF-DLSH-----GSAQVKGHGKKVADALTNAVAHVDD-----MPNAL YFPHF-DLSH-----GSAQVKAHGKKVGDALTLAVGHLDD-----LPGAL FFPKFKGLTTADQLKKSADVRWHAERIINAVNDAVASMDDTEK--MSMKL KFDRFKHLKTEAEMKASEDLKKHGVTVLTALGAILKKKGH-----HEAEL LFSFLKGTSEVPQ--NNPELQAHAGKVFKLVYEAAIQLQVTGVVVTDATL ! * *** ** ***!**** * ** *** *

85 85 80 80 98 86 90


ATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVA AALSELHCDKLHVDPENFRLLGNVLVVVLARHFGKDFTPELQASYQKVVA SALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLA SNLSDLHAHKLRVDPVNFKLLSHCLLSTLAVHLPNDFTPAVHASLDKFLS RDLSGKHAKSFQVDPQYFKVLAAVIADTV---------AAGDAGFEKLMS KPLAQSHATKHKIPIKYLEFISEAIIHVLHSRHPGDFGADAQGAMNKALE KNLGSVHVSK-GVADAHFPVVKEAILKTIKEVVGAKWSEELNSAWTIAYD !* *!* ** *** ** ** * *** * *** * *


GVANALAHKYH-----GVANALAHKYH-----SVSTVLTSKYR-----SVSTVLTSKYR-----MICILLRSAY------LFRKDIAAKYKELGYQG ELAIVIKKEMNDAA--* * **

135 135 130 130 139 136 139

146 146 141 141 149 153 153

Fig. 5.5. Same as Figure 5.1, except that the latter was computed using the heuristic program clustalw [246], while the optimal program msa [98] was used to obtain the present alignment.

In order to determine the minimum score a vertex might have and still be on the path corresponding to an optimal alignment, msa first uses a heuristic alignment algorithm before beginning the search for the optimal alignment [169, 98]. Hence

5.3 Heuristic Multiple Alignment

97

heuristic alignment methods appear to be a prerequisite for practical optimal multiple sequence alignment algorithms. Further, they are by far the most widely used class of multiple alignment methods and we turn to them next.

5.3 Heuristic Multiple Alignment Heuristic multiple sequence alignment programs proceed in essence by computing only pairwise alignments. The trick is to compute these pairwise alignments in an order corresponding to the evolutionary relationships of the sequences to be aligned. Perhaps the most popular approach to heuristic alignment is known as progressive alignment. The method was developed in 1987 by Feng and Doolittle [74] and is the basis of such programs as clustalw, which we used to compute the alignment shown in Figure 5.1. At the time when Feng and Doolittle published their proposal, the standard method for constructing multiple sequence alignments was to generate all pairwise alignments and then to assemble the multiple alignment by hand without considering the evolutionary relationships of the sequences. The authors commented on this: “It seems folly to us that a gap should be discarded in an alignment of two closely related sequences merely because an alignment with some distantly related sequence might be improved” [74, p. 352]. Since similar sequences can be aligned more reliably than dissimilar sequences, the alignment of similar sequences should be given more weight in the overall alignment scheme. This increased weight is achieved by aligning the sequences progressively, starting from the most similar pair and following the rule “once a gap, always a gap” [74]. In other words, gaps introduced early in the alignment process are not changed later on. Pairwise Phase 0 17 17 25 10

0 4 25 17

0 25 17

0 25

0

distance

Multiple Sequence Phase pre-existing gap

new gap

Fig. 5.6. Progressive multiple sequence alignment.

Figure 5.6 gives a graphical summary of the progressive alignment method. First, all pairwise alignments are constructed and the pairwise distances between the sequences are calculated from these alignments. A simple distance measure would be

98


the number of pairwise mismatches. The order in which individual sequences or sets of aligned sequences progressively join the target alignment is then determined by the construction of a cluster diagram from the distance matrix. In this cluster diagram, also referred to as a “guide tree” [246], leaves represent sequences and internal nodes represent alignments. Starting from the most similar pair of sequences, the algorithm works from the leaves to the root of the guide tree. In the example shown in and are the two most similar sequences. They get aligned first, folFigure 5.6 lowed by and . The next node up the tree clusters the two pairwise alignments is added. formed so far and in the final step With the aid of a scoring scheme and a pairwise alignment method it is not difficult to align pairs of alignments and an example of this is shown in Figure 5.7. However, during the multiple sequence phase of heuristic alignment, the intermediate alignments are often represented as so called profiles (Chapter 6) rather than the standard alignments suggested by Figure 5.6. Result:

Alignments to be aligned: AACGT A-CGT AAGT 2. A-GT

AACGT A-CGT AA-GT A--GT

1.

-

A A

A -

C C

G G

T T

A A A G G T T Fig. 5.7. Global alignment of two alignments using dynamic programming. Gaps in the existing alignments are ignored, that is, their alignment carries a score of zero. In this example match , mismatch , and gap . The traceback path corresponding to the final alignment is marked in bold.

5.4 Summary Multiple sequence alignments are often computed for known members of a protein family. The aim is to discover sequence motifs that are conserved across the mem-

5.6 Exercises and Study Questions

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bers of the protein family in order to infer the functionally important protein domains. Like pairwise sequence alignments, multiple sequence alignments can be computed using optimal or heuristic methods. In practice most multiple sequence alignments are computed using heuristic methods. These proceed by reducing the multiple alignment problem to a series of pairwise alignments. The order in which these pairwise alignments are fused into the multiple alignment is crucial for the success of the algorithm. The pairwise alignments of the input sequences are used to calculate pairwise distances. This in turn serves as input data for the construction of a guide tree, which determines the order in which sequences are added to the multiple alignment.

5.5 Further Reading A number of comprehensive surveys of multiple alignment methods have been published over the years [176, 247, 62]. In addition, Chapter 14 in Gusfield’s textbook treats many of the algorithmic aspects of multiple sequence alignment [99].

5.6 Exercises and Study Questions 5.1. How would you interpret the fact that at position 11 of the globin alignments in Figures 5.1 and 5.5 all seven sequences have a leucine (L)? 5.2. Try to verify that the myoglobin molecule depicted in Figure 5.2 contains the eight alpha helices marked in the myoglobin sequence of the globin multiple sequence alignment (Fig. 5.1). 5.3. Write down the hyperlattice for aligning the three sequences AATG, ATG, and TG and trace the path corresponding to the alignment AATG A-TG --TG

5.4. Write the alignment path traced in the previous exercise as a series of triplets of coordinates in the hyperlattice. 5.5. Calculate the score of the alignment using the sum-of-pairs method and , match , gap . Remember that the alignment of two mismatch gaps is ignored (scored as zero). 5.6. Can you think of an alternative cooptimal path (and hence alignment)? 5.7. In the hyperlattice corresponding to the alignment in Figure 5.1, what are the ? coordinates of the vertex following position

100


5.8. What is the exact number of vertexes in the hyperlattice necessary for computing an alignment of the seven globins shown in Figure 5.1 by dynamic programming? 5.9. Given that each operation in a “na¨ıve” version of the optimal multiple alignment algorithm takes one nanosecond, how many sequences of length 100 can be aligned in a decade? 5.10. Find all the differences between the alignments shown in Figures 5.1 and 5.5. 5.11. The first step in “progressive” alignment algorithms is the formation of all possible pairwise alignments. Given sequences to be aligned, how many such pairwise alignments need to be computed? 5.12. Aligning the translation of a set of DNA sequences using clustalw is faster than aligning the corresponding DNA sequences. Why is this the case and how much faster is protein alignment compared to DNA alignment? 5.13. Use the dynamic programming matrix shown in Figure 5.7 to determine an alignment that is cooptimal to the one shown.

6 Sequence Profiles and Hidden Markov Models Biologists should realize that before long we shall have a subject which might be called ’protein taxonomy’—the study of the amino acid sequences of the proteins of an organism and the comparison of them between species. Francis H. C. Crick [42, p. 142]

Sequence profiles summarize the information contained in a multiple sequence alignment. Such a summary can then be used to search for other members of the sample contained in the underlying multiple sequence alignment. Hidden Markov models are stochastic models originally developed in the context of speech recognition. It was later realized that sequence profiles can be formulated as hidden Markov models, thereby introducing powerful new methods to sequence analysis. In this chapter we first introduce sequence profiles before turning our attention to their reformulation in the context of hidden Markov models.

6.1 Profile Analysis Profile analysis is centered on protein families, which are groups of proteins that have similar functions. An example of such a protein family is the globins, all of which are involved in oxygen transport. A phylogeny of seven members of this protein family is depicted in Figure 6.1. Members of a protein family may or may not all be connected by significant pairwise alignments. For example, if we carry out a standard BLAST search with human -globin as the query sequence marked in Figure 6.1, we get significant hits up to the evolutionary distance of sperm whale myoglobin (boxed sequences in Figure 6.1). However, we observe no hit to leghemoglobin. Hence, in such a pairwise search, the homology between -globin and leghemoglobin would go undetected. Database search programs such as blastp, a member of the BLAST package for comparing pairs of protein sequences, employ score matrices such as the PAM and BLOSUM series. These matrices are position independent, that is, they are applied with equal weight to every position in an alignment. In contrast, profiles are position-specific score matrices that capture the primary structure of a set of proteins belonging to the same family. In order to demonstrate the power of profile analysis, consider again our globin example. PROSITE is a database containing profiles characteristic of protein families [226]. A scan of human -globin against PROSITE

102

6 Sequence Profiles and Hidden Markov Models

human -globin horse -globin human -globin

Query

horse -globin lamprey globin 5 whale myoglobin lupine leghemoglobin Fig. 6.1. Phylogeny of seven members of the globin protein family. Boxed sequences are those detected by comparing human -globin against the complete set of seven sequences.

returns a match to the globin profile, and a match to the plant-globin profile, which contains leghemoglobin from yellow lupine (Fig. 6.1). Hence, profile analysis can detect homologies across greater evolutionary distances than standard pairwise alignment. Profile analysis is carried out in two steps: (i) construction of the profile and (ii) comparison of the profile with a single sequence or a database of sequences. Profile construction always starts from an alignment of multiple homologous sequences. The simplest method for extracting a profile from such a multiple sequence alignment is to count at each position in the alignment the frequency of each of the possible 20 amino acids. The result of subjecting the first 20 positions of our globin alignment (Fig. 5.1) to such an analysis is shown in Figure 6.2. The profile consists of 20 columns, one for each possible amino acid, and a row for each position in the alignment. For example, at position 10 we find three valines (V) and one each of alanine (A), histidine (H), proline (P), and glutamine (Q). More elaborate scoring schemes that incorporate the standard amino acid score of matrices have been proposed [90]. For example, the weighted score amino acid at position can be defined as

where is the weight of amino acid at position and is the score of aligning amino acids and as determined from an amino acid substitution matrix. If the weighting scheme is linear, is the frequency of amino acid in column of the multiple alignment from which the profile is computed. With a logarithmic of the frequency would be used with the added proviso that weighting scheme the

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103

Position A C D E F G H I K L M N P Q R S T V W Y 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

0 0 0 0 0 0 0 0 1 1 0 0 2 3 0 1 2 2 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 2 2 4 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 1 0 3

0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 2 0 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0

1 0 0 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 1 0 0 0 0 5 0 1 0 0 1 0 0 0

0 0 0 0 1 0 0 0 0 0 0 2 0 0 0 0 3 0 0 1

0 0 1 0 0 0 0 1 2 3 0 0 0 0 0 0 0 0 6 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Fig. 6.2. Frequency count of the first 20 positions of the globin alignment shown in Figure 5.1. The boxed entries are discussed in the text.

if an amino acid is missing from a particular column, it is counted as being present once. The result of applying the linear scoring scheme together with an amino acid score matrix (Table 6.1) to the first 20 positions of our globin alignment is shown in Figure 6.3. The boxed entries are the same as those boxed in the simple profile of Figure 6.2. In order to understand the entries in the matrix shown in Figure 6.3, consider its first line. Since there is only proline (P) present in the first column of the P , where is the sequence alignment, all entries in the profile are simply and hence the number of sequences, i.e. seven in our case. For example, A P . entry in the first line and the first column of the profile is Notice that the last column of the profile gives a position-specific gap-score. The first nine positions of the alignment contain gaps (Fig. 5.1), which is reflected by a and those (Fig. 6.3). jump in the gap scores between positions Aligning a sequence to a profile can be done using the standard local dynamic programming technique. The only difference is that scores are now looked up in the profile. Consider for example the multiple DNA sequence alignment in Figure 6.4A. and mismatch , we obtain the correspondGiven a score scheme of match ing profile shown in Figure 6.4B. Figure 6.5 shows how this profile can be aligned with the example sequence ACCT. The sequence is written along the horizontal axis of the dynamic programming matrix and the profile along the vertical axis. We carry out a local alignment, so the first row and column are initialized with zeros. As before, gap extensions are scored as -1, and gap opening as 0. The score of a match

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Table 6.1. Amino acid substitution matrix for profile analysis [89]. Match scores are shown in bold. A 15 2 3 3 3 -5 6 -1 0 0 -1 0 2 5 2 -3 4 4 2 -8 -3 2

B 2 11 -4 11 6 -6 6 4 -2 4 -5 -3 11 1 5 1 3 2 -2 -6 -3 6

C 3 -4 15 -5 -6 -1 2 -1 2 -6 -8 -6 -3 1 -6 -3 6 2 2 -12 10 -16

Position

A

C

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

0.7 0.0 0.2 0.4 0.5 0.8 0.5 0.2 3.5 3.8 -1.0 4.0 7.4 8.7 2.8 1.0 6.8 4.5 1.7 -0.1

0.1 0.2 0.2 -0.7 0.2 0.2 0.8 0.2 1.2 0.4 -8.0 4.8 -0.2 0.7 -5.7 -5.5 1.7 -3.1 2.0 -5.0

A B C D E F G H I K L M N P Q R S T V W Y Z

D 3 11 -5 15 10 -10 6 4 -2 3 -5 -4 6 1 6 0 2 2 -2 -11 -5 8

E 3 6 -6 10 15 -6 5 4 -2 3 -3 -2 5 1 6 0 2 2 -2 -11 -5 11

F -5 -6 -1 -10 -6 15 -6 -1 6 -6 12 5 -5 -6 -8 -5 -3 -3 2 12 13 -6

G 6 6 2 6 5 -6 15 -2 -3 -1 -5 -3 4 3 2 -3 6 4 2 -10 -6 3

H -1 4 -1 4 4 -1 -2 15 -3 1 -2 -3 5 2 6 5 -2 -1 -3 -1 3 5

I 0 -2 2 -2 -2 6 -3 -3 15 -2 8 6 -3 -2 -3 -3 -1 2 11 -5 1 -2

K 0 4 -6 3 3 -6 -1 1 -2 15 -3 2 4 1 4 8 2 2 -2 1 -6 4

D

E

F

G

H

I

K

0.1 -0.2 -0.2 2.1 0.2 0.8 0.2 -0.2 0.7 1.1 -5.0 2.0 4.8 5.2 10.8 1.0 2.8 1.5 -2.0 0.1

0.1 -0.2 -0.2 1.4 0.2 0.7 0.2 -0.2 0.5 1.1 -3.0 2.0 6.1 6.5 12.2 1.0 2.8 1.8 -2.0 0.7

-0.8 0.8 0.2 -1.4 -0.4 -0.8 -0.4 0.2 -1.0 -2.0 12.0 -3.0 -5.7 -5.1 -7.4 -3.2 -4.2 -0.2 2.5 -0.2

0.4 -0.4 0.2 0.8 0.5 2.1 0.8 0.2 3.5 2.1 -5.0 5.4 6.1 7.0 4.8 -1.2 4.5 1.2 1.2 -1.7

0.2 -0.4 -0.4 0.5 -0.1 -0.2 -0.2 -0.4 -1.2 1.8 -2.0 -1.7 1.1 0.1 4.2 0.4 -0.1 0.7 -3.0 0.4

-0.2 2.1 1.5 -0.2 0.2 -0.4 -0.1 1.5 2.7 3.5 8.0 -0.1 -1.5 -1.1 -2.1 -2.1 0.2 1.1 11.5 1.2

0.1 -0.2 -0.2 0.4 0.2 -0.1 0.2 -0.2 -0.7 0.0 -3.0 2.0 1.0 1.0 3.1 10.8 1.7 2.4 -2.0 7.0

L -1 -5 -8 -5 -3 12 -5 -2 8 -3 15 12 -4 -3 -1 -4 -4 -1 8 5 3 -2

M 0 -3 -6 -4 -2 5 -3 -3 6 2 12 15 -3 -2 0 2 -3 0 6 -3 -1 -1

N 2 11 -3 6 5 -5 4 5 -3 4 -4 -3 15 0 4 1 3 2 -3 -3 -1 4

P 5 1 1 1 1 -6 3 2 -2 1 -3 -2 0 15 3 3 4 3 1 -8 -8 2

Q 2 5 -6 6 6 -8 2 6 -3 4 -1 0 4 3 15 4 -1 -1 -2 -5 -6 11

R S -3 4 1 3 -3 6 0 2 0 2 -5 -3 -3 6 5 -2 -3 -1 8 2 -4 -4 2 -3 1 3 3 4 4 -1 15 1 1 15 -1 3 -3 -1 13 3 -6 -4 2 0

L

M

N

P

Q

-0.4 1.1 1.1 -0.7 -0.1 -0.7 -0.5 1.1 1.4 2.4 15.0 -3.1 -2.7 -2.5 -3.2 -1.5 -1.4 2.4 8.0 2.2

-0.2 0.8 0.8 -0.5 0.0 -0.4 -0.4 0.8 1.2 1.8 12.0 -2.1 -1.5 -1.4 -2.2 1.0 -0.4 2.8 6.0 4.5

0.0 -0.4 -0.4 0.8 0.2 0.5 0.4 -0.4 0.0 0.2 -4.0 2.7 2.5 3.2 5.1 2.7 2.4 4.2 -3.0 1.0

2.1 -0.2 0.1 0.1 0.4 0.4 0.5 0.1 1.4 4.0 -3.0 3.7 6.4 3.4 1.2 0.2 3.7 0.7 0.5 0.4

0.4 -0.4 -0.2 0.8 -0.1 0.2 -0.1 -0.2 0.0 2.8 -1.0 -1.0 3.4 2.7 7.2 2.4 2.1 2.0 -2.1 1.8

T 4 2 2 2 2 -3 4 -1 2 2 -1 0 2 3 -1 -1 3 15 2 -6 -3 1

V 2 -2 2 -2 -2 2 2 -3 11 -2 8 6 -3 1 -2 -3 -1 2 15 -8 -1 -2

W -8 -6 -12 -11 -11 12 -10 -1 -5 1 5 -3 -3 -8 -5 13 3 -6 -8 15 11 -8

Y -3 -3 10 -5 -5 13 -6 3 1 -6 3 -1 -1 -8 -6 -6 -4 -3 -1 11 15 -6

Z 2 6 -6 8 11 -6 3 5 -2 4 -2 -1 4 2 11 2 0 1 -2 -8 -6 11

R

S

T

V

W

Y

-

0.4 -0.4 -0.4 0.0 -0.1 -0.4 0.1 -0.4 -1.7 0.0 -4.0 0.4 -0.4 -1.5 0.5 7.1 -0.5 -0.5 -3.0 4.2

0.5 -0.1 -0.1 0.2 0.4 0.8 2.1 -0.1 1.1 0.2 -4.0 11.5 3.7 5.2 1.5 2.4 4.4 1.1 -1.0 0.2

0.4 0.2 0.2 0.2 2.1 0.5 0.4 0.2 1.7 1.5 -1.0 6.4 3.1 3.2 1.5 1.1 7.8 1.7 2.0 2.5

0.1 1.5 2.1 -0.2 0.2 0.2 -0.1 2.1 4.8 6.1 8.0 -0.1 0.5 0.4 -2.0 -2.2 1.0 1.7 14.4 1.2

-1.1 -0.7 -1.1 -1.5 -0.8 -1.4 0.4 -1.1 -4.8 -6.5 5.0 0.4 -9.1 -7.5 -10.1 1.7 -5.1 -1.5 -7.5 2.8

-1.1 0.1 -0.1 -0.7 -0.4 -0.8 -0.5 -0.1 -1.5 -2.4 3.0 -3.7 -5.4 -4.1 -5.1 -3.1 -3.5 -1.1 -0.7 -3.0

1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5

Fig. 6.3. Profile of the first 20 positions of the globin alignment shown in Figure 5.1. For details see text. The profile was calculated using the EMBOSS software package [209].

6.1 Profile Analysis

105

between position in the profile and in the sequence is looked up as entry in CT and ; both carry a the profile. The two cooptimal best local alignments are AC AC CT score of 0.8.

1 A A A A C

2 C C C G G

A 3 4 GC GC GC GG GT

Position 1 2 3 4 5

5 A T T T T

B A C 0.6 -0.6 -1 0.2 -1 -1 -1 0.2 -0.6 -1

G -1 -0.2 1 -0.6 -1

T -1 -1 -1 -0.6 0.6

Fig. 6.4. A: Alignment of five DNA sequences with alignment positions given in the top row. B: Profile computed from the alignment using a score scheme of match and mismatch .

Position A 0 0

G 0

T 0

0

A 0

C 0

C 0

T 0

1

0.6 -0.6 -1

-1

0

0.6

0

0

0

2

-1 0.2 -0.2 -1

0

0

0.8

0

0

3

-1

0

0

0

0

0

4

-1 0.2 -0.6 -0.6 0

0

0

0.2

0

0

0

0

0.8

5

C 0

-1

-0.6 -1

1

-1

-1 0.6 0

Fig. 6.5. Local alignment between a profile (vertical axis) and a DNA sequence (horizontal axis.

We now turn to a more realistic application of profile analysis. Imagine that we have identified the six boxed globin sequences shown in Figure 6.1 by pairwise homology search. In order to look for further members of our protein family, we compute a profile from these six sequences. An alignment of leghemoglobin to this profile happens to result in a score of 725.36. Is this significant? One method to determine this is to compare the original score to the distribution of scores obtained by aligning unrelated proteins to the profile. Alternatively, the sequence of leghemoglobin can be repeatedly randomized and realigned to the profile. The result of 1,000 such randomizations is shown in Figure 6.6A, which implies a marginal significance level of 0.009. If we now iterate the analysis and include leghemoglobin in the profile computation, we get the result shown in Figure 6.6B. It is clear that in this case the original score is due to highly significant homology between leghemoglobin and the globin profile. The superior sensitivity of profiles relative to pairwise comparisons is the reason for using profiles as intermediate steps in heuristic multiple sequence alignment algorithms such as clustalw [246].

106


A

B

60

60

Count

80

Count

80

40

40

original score 20

0 400

original score

20

450

500

550

600

650

700

750

800

850

Score

900

950

0 400

450

500

550

600

650

700

750

800

850

900

950

Score

Fig. 6.6. The sequence of leghemoglobin was shuffled 1,000 times and aligned to the globin profile. The resulting distribution of alignment scores can then be compare to the score of the alignment between the profile and the unshuffled leghemoglobin sequence (original score). A: Profile computed from the six boxed globin sequences shown in Figure 6.1. B: Profile computed from all seven globins shown in Figure 6.1. Profile generation and profile alignment was carried out using the EMBOSS programs prophecy and prophet, respectively [209].

We have already mentioned the limited sensitivity of classical pairwise alignment programs such as blastp. However, the BLAST package also contains a program based on profile searches called psi-blast [10]. It works by iterating pairwise searches. After each round of pairwise searching, a profile is calculated from the set of homologous sequences and used as query for searching in the next round. The iteration stops once no new homologues are found. When using human -globin as query against the SwissProt database, psi-blast converges after five rounds of iteration with a results list that contains a highly significant hit to leghemoglobin. No such hit was contained in the results list after the first round of searching. Hence psi-blast considerably extends the reach of database searches in homology space. Profile analysis had been applied to sequence analysis problems for a few years when it was realized that profiles can be rewritten as hidden Markov models [155]. These are stochastic models that can be applied to the analysis of diverse kinds of sequential data, most notably human speech and biological sequences.

6.2 Hidden Markov Models Hidden Markov models (HMMs) are a class of stochastic models that are widely used in computational biology. Perhaps their most successful applications are to gene prediction and homology detection. Homology detection by HMMs is covered in Section 6.3, while gene prediction is the topic of Chapter 7. In the following we first explain what a hidden Markov model is and how it can be used to analyze sequential data including DNA and protein sequences. This is followed by a survey of a special kind of HMM, profile HMM, which is derived from a

6.2 Hidden Markov Models

107

multiple sequence alignment. Profile HMMs are widely applied in protein classification and can themselves be used to rapidly calculate multiple sequence alignments. Hidden Markov models were made accessible to the scientific community in a classical tutorial by Rabiner [205] and we follow his treatment. Consider a system, distinct states, e.g. “healthy” such as your body, which can take one of and “ill”. Let a certain state at time be called . A discrete, -th order Markov chain has the property that a state at time depends on the system’s states at times . Here we will only be concerned with first order Markov chains. Dealing with a first order Markov chain means that the probabilistic description of a state sequence is truncated to the present and the previous state:

As the system evolves from time to time , it switches between states according to time-independent probabilities of the form

where and may be identical. Figure 6.7 depicts our healthy vs. ill example as a Markov chain with two states. The transition probabilities marked in Figure 6.7 might alternatively be written as

8

()*+ /.-, h

( '&%$ !"# e

Fig. 6.7. Markov chain with two states “healthy” ( ) and “ill” ( ).

Given that you are healthy now, we can ask, what is the probability that you will be healthy – healthy – healthy – ill – ill over the next five days? The answer is . Perhaps more interestingly, we can ask, what is the average duration, , of state ? This can be found by summing the probabilities of all possible durations of a certain state

That is, our Markov chain in Figure 6.7 implies an expected 100 consecutive days of consecutive days of illness, if states can switch once a day. health and In many situations it is necessary to distinguish between a system’s state and the value of an observable quantity which depends on that state. For example, a common indicator of health or otherwise is our body temperature. Hence, we call “healthy”

108


()*+ /.-, = { { K {{ { {{ {{ startC CC CC CC CC ! !"# '&%$ E

/ n: 0.8, e: 0.2

/ n: 0.15, e: 0.85

Fig. 6.8. Hidden Markov model with hidden states “healthy” ( ) and “ill” ( ). Each hidden state emits one of two possible observable states, normal (n) and elevated (e) body temperature with the indicated probabilities.

and “ill” hidden states which emit the observable states “normal” (n) or “elevated” (e) body temperature (Fig. 6.8). Such a model can be used to generate a series of binary temperature recordings: nnneennnnnn... Before we learn how to generate such an observation sequence using our HMM, let us summarize its elements: 1.

: the number of states

2.

: the number of observational symbols, that is, the size of alphabet

3.

; a state at time is referred to as

: state transition probabilities

4.

,

: observation symbol probabilities; , : initial state probabilities;

5.

at

,

,

The complete parameter set is conveniently referred to as

If we now return to our task of generating an observation sequence

where all 1. 2. 3. 4.

, we can use the following procedure:

choose according to ; choose according to change to new state according to if , goto 2; else stop

;

We demonstrate the application of this procedure using the HMM of Figure 6.8 and the following list of random numbers:

6.2 Hidden Markov Models

109

0.66287 0.505421 0.495442 0.568873 . . . The algorithm starts at “start” in Figure 6.8. By sequentially using up the random numbers and following the steps in the algorithm it yields Step 1: Step 2: Step 3: Step 2:

, therefore , therefore , therefore , therefore

n n

Now that we are acquainted with the nuts and bolts of HMMs, we can start using them to solve interesting problems. To fix our ideas, consider the HMM in Figure 6.9, which models in a simplified fashion G/C- and A/T-rich genomic regions. Figure 6.9 also contains an observation sequence, a DNA sequence. It is convenient to think of such a sequence as having been generated by a HMM, even though in most situations of practical relevance it would be real sequence data. The metaphor of a HMM generating a given biosequence is a shorthand for saying that the HMM is believed to model important properties of this sequence. Given such a model and the corresponding empirical data we can solve the following three fundamental problems for HMMs: 1. The scoring problem: given and , compute ; in the context of our example in Figure 6.9 we wish to know the probability that a given DNA sequence has been generated by a known HMM. 2. The detection problem: given and , find the optimal state sequence; for our example this means that we wish to know which parts of the sequence are A/Trich and which are G/C-rich. by adjusting 3. The training problem: given and an initial , maximize the model’s transition and emission probabilities (not its architecture of hidden states); for our example we would start with DNA sequence data containing A/Tand G/C-rich regions and would try to determine the corresponding emission and transition probabilities. All three problems can be solved efficiently and in the following we explain the solutions to the scoring and the detection problem. Readers interested in the slightly more advanced training problem are referred to Rabiner’s article [205]. A na¨ıve method for solving the scoring problem would be to write down all possible state sequences, calculate the probability of each one and choose the state sequence with the highest probability. However, the number of state sequences is , which renders this approach unfeasible. Fortunately, a bottom up approach similar to that encountered in the context of computing the number of possible global alignments (Section 2.5) can also be applied in this case and is known as forward rows and columns and deterprocedure. We fill a two-dimensional table with as follows: mine its entries 1. initialization:

110


()*+ /.-, =K { {{ {{ { { {{ startC CC CC CC CC ! ()*+ /.-, E

/ A/T: 0.8, G/C: 0.2

/ A/T: 0.3, G/C: 0.7

Observed T G C T A T T G G T Hidden Fig. 6.9. Hidden Markov model of A/T and G/C-rich genomic regions and The sequence of observed and hidden states was generated using this model.

2. recursion: 3. termination:

,

, respectively.

,

Figure 6.10 shows the first two columns in the table of the data and HMM of Figure 6.9.

values corresponding to

T G 0.72 0.14154 0.03 0.02961 Fig. 6.10. Calculation of the probability of an observation sequence using the forward procedure. The data and underlying HMM are shown in Figure 6.9.

In contrast to the scoring problem, the detection problem contains an element of optimization: we wish to find the state sequence that best explains the observation sequence. A dynamic programming approach known as Viterbi algorithm can be used to efficiently find the most likely sequence of hidden states. This algorithm is based on a similar table as employed in the forward procedure. 1. initialization: , 2. recursion: addition, keep back pointer to 3. back tracking: Start from

,

,

; in

and follow the back pointers

Application of this algorithm to our DNA sequence example (Fig. 6.9) yields the dynamic programming matrix of Figure 6.11. Notice that the most likely state sequence

6.3 Profile Hidden Markov Models

111

differs from the known state sequence in Figure 6.9 due to the stochastic nature of the model. T 0.7200 0.0300

G 0.1411 0.0195

C 0.0277 0.0127

T ... 0.0217 ... 0.0035 ...

Fig. 6.11. Dynamic programming matrix used in the Viterbi algorithm according to the DNA sequence and HMM shown in Figure 6.9. Back pointers for the most likely state sequence are also shown.

The probabilities in the programming tables of Figures 6.10 and 6.11 rapidly become very small and lead to numerical underflow in computer programs. The corresponding algorithms are therefore always implemented on the basis of logprobabilities. Figure 6.9 suggests how HMMs might be useful in determining G/C-rich regions, which are also often gene-rich. However, the models we have looked at so far are unrealistically simple. In addition, they have the property of being fully connected, . As we will see in Section 6.3, profile that is, all transition probabilities are HMMs differ on both counts.

6.3 Profile Hidden Markov Models As explained in Section 6.1, profiles model protein families through position-specific residue scores as well as gap penalties. Such position-specific scores can then be used to detect more subtle homologies than is possible using pairwise alignments. In the mid 1990s it was realized that profiles could be rewritten as HMMs usually known as profile HMMs, thereby making profile analysis more rigorous [155]. Profile HMMs consist of the three elements of an alignment: matches, insertions, and deletions. As shown in Figure 6.12, these elements are interpreted as hidden states in the HMM. The delete state is the simplest state; it emits only a single symbol, the gap symbol. The insertion state, , emits amino acids drawn randomly from their background frequencies. The match state, finally, emits amino acids according to their frequencies at a given alignment position. It is important to realize that the match state is position-specific and this property distinguishes it from the positioninvariant insertion and deletion states. In fact, the emissions of the match state correspond to the profile constructed from the underlying multiple sequence alignment, hence, the name profile HMMs. Complete profile HMMs are built from a series of the elements shown in Figure 6.12 and an example model is displayed in Figure 6.13. The first thing to notice is that it is directed from a “start” to an “end” state and contains no “backward” transitions. The number of match states is equal to the length of the profile and the probabilities of the emission states are derived from the profile entries.

112


A:

A ,...,Y:

/.-, ()*+ JW o

Y

O !"# z 6 '&%$

A:

A ,...,Y:

Y

'&%$ !"#v D o

Fig. 6.12. Precursor of profile hidden Markov model. : observed frequency of amino acid ; : background frequency of amino acid ; : delete state; : insertion state; : match state.

In contrast to profiles, which may contain arbitrary gap scores as well as pseudocounts, all free parameters of HMMs, including position-specific gap scores, are derived from the data in a consistent fashion. These parameters correspond to probabilities assigned to the arrows in Figure 6.13. Given the complexity of protein structure, it might appear surprising that a first order rather than a higher order Markov process should be able to account for protein structure. It turns out that profile HMMs are at least as sensitive as traditional profiles in the detection of distant homologues [54]. In addition, they are not only derived from multiple sequence alignments, but can themselves be used to align hundreds of sequences very efficiently. A:

A ,...,Y:

O

A:

Y

A ,...,Y:

O

Y

7654 7654 / 0123 gg3 0123 > AXXXX fff3 > AWWW ggggg }}} AAA XXXXXXXXX fffff }}} AAA WWWWWWWWW XfXfXfXfXffff ggggg WWWWW g A AA } } g g f g A X } } f XXXXX } WWWWW AAfffffff AA ggggg XX} }} WWWW+ AA ggggg fffff AAA }} }} XXXX/+0123 7654 7654 / 0123 / end A } } start L P 7 7 P P P n n A A } } 9 P n n P LL A A } } PPP n n PPP ss n n A A } } LL n n PPP AA PPP AA } nnn } nnn ss LL s } } s PPP AA PPP AA } nnn } nnn LL s } } s PPPA ss PPPA }}nnn LL }}nnn % n ' s '()*+ ()*+ /.-, /.-,n ()*+ /.-, C C C

A:

A ,...,Y:

Y

A:

A ,...,Y:

Y

A:

A ,...,Y:

Y

Fig. 6.13. Example profile hidden Markov model for protein sequences. Adapted from [155]. : observed frequency of amino acid ; : background frequency of amino acid ; : delete state; : insertion state; : match state.

Searching for homologous sequences using hidden Markov models is conceptually simple: Given a set of profile HMMs characterizing protein families and an anonymous protein sequence, compare the sequence to each HMM in turn using the likelihood returned by the forward procedure as the alignment’s score. A significant score then indicates which protein family the anonymous sequence belongs to.

6.4 Summary

113

There are databases of profile HMMs that are routinely used for this purpose. The best known of these is perhaps the protein family database PFAM [17]. While the forward procedure solves the problem of homology determination, the Viterbi algorithm can be used to compute alignments. Consider the two protein GAHYA and GHA and let and sequences be the corresponding most likely state sequences. The alignment implied by these state sequences is then obtained by writing amino acids emitted by the same match state in the same column. For our example the connections {{ {{ { { {{

{{ {{ { { {{

yield the alignment GAHYA G-H-A This procedure can be extended to an arbitrary number of sequences. For example, HYA with the most likely state sequence if we wanted to add the sequence to our alignment, we would get GAHYA G-H-A --HYA Notice that an insertion in one sequence leads to the introduction of a gap into all other sequences. In contrast, a deletion produces a gap only in the sequence concerned. The Viterbi algorithm runs in time proportional to the length of the profile HMM, whose number of hidden states in turn is proportional to the length of the input sequence. Hence, the alignment procedure just described runs in time proportional to the combined lengths of the input sequences. This contrasts with the standard pro, gressive multiple sequence alignment algorithm, which runs in where is the length of the input sequences. However, in spite of this computational efficiency, HMMs are in practice rarely used for calculating multiple sequence alignments. The reason for this is that a good HMM needs to be calculated in the first place, which tends to require a large number of sequences and is computationally intensive. For most multiple sequence alignment tasks the progressive algorithm implemented in programs such as clustalw [246] generates more reliable results than alignments based directly on HMMs [247].

6.4 Summary Profiles are position-specific score matrices derived from multiple sequence alignments. They are typically used for protein classification, where an unknown amino

114


acid sequence is compared to a set of profiles characterizing known protein families. Such comparisons between profiles and anonymous sequences are often more sensitive than pairwise comparisons. Profiles can be rewritten as profile hidden Markov models. Hidden Markov models are based on Markov chains. These consist of a number of states and the probabilities of switching between them. The first order Markov chains considered in this chapter have the property that the chain’s state at some point in time depends only on its direct predecessor state. In hidden Markov models (HMMs), the states of the Markov chain are said to be hidden and to emit observation states, for example a DNA sequence. Given such data and a HMM, there are three classical problems that need to be solved for HMMs to be useful in biological sequence analysis: the scoring, the detection, and the training problem. Efficient solution of these problems leads to many applications of HMMs in biology, including homology detection, for which profiles were originally designed, but also extremely rapid multiple sequence alignment.

6.5 Further Reading The tutorial by Rabiner is a very good place to learn more about hidden Markov models [205]. The textbook by Durbin and colleagues explains in detail how hidden Markov models are applied in biological sequence analysis [52].

6.6 Exercises and Software Demonstration 6.1. Write down the entries for position 21 in the profile shown in Figure 6.2. 6.2. Consider the Markov chain 8

()*+ /.-, i

(/.-, ()*+ e

(6.1)

describing the transitions between the states “ill” ( ) and “healthy” ( ). States can switch at daily intervals. Given that a person is ill now, what is the probability of over the next five days? observing the sequence 6.3. What is the expected duration of illness and health according to HMM (6.1)? 6.4. Consider the HMM shown in Figure 6.8. The following six random numbers are drawn from a uniform distribution between 0 and 1: 0.222969, 0.731557, 0.583197, 0.547333, 0.821281, 0.163268. Use these numbers to generate the first three hidden and observed states of the model. 6.5. Fill in column 3 in the forward process matrix of Figure 6.10. 6.6. Fill in column 5 in the Viterbi matrix of Figure 6.11.

6.6 Exercises and Software Demonstration

115

6.7. Given the DNA sequences ATGA, AGA, and TGT as , well as the corresponding most likely state sequences , and , what is the corresponding multiple sequence alignment? ATGA and 6.8. Given the sequences most likely state sequences sequence alignment unambiguous?

ACA, as well as the corresponding and , is the induced

6.9. Work through the tutorial of the bioinformer program HMM Markov Model given in Section A.3.1.

Hidden

sdfsdf

7 Gene Prediction Rates of spontaneous mutation to recessive lethal and visible mutants in mammals are of the order of 10 –6 to 10 –5 per locus per generation. If there are 40,000 genes, the total rate of mutation to lethal or nonfunctional alleles would be between 4 and 40 percent per gamete. From this consideration alone, it is clear that there cannot be more than 40,000 genes. Jack Lester King and Thomas H. Jukes [145, p.794]

7.1 What is a Gene? The old question of “what is a gene” has been answered differently since their discovery by Mendel in 1866. Mendel thought of genes as mathematical objects and did not speculate on their material correlate. In 1927, Muller discovered that X-rays could induce mutations in Drosophila and this hinted that genes consisted of matter that interacted with X-rays. In addition, cytological studies correlated the action of genes with the structure and transport of chromosomes. However, chromosomes consist of both proteins and DNA and it was unclear which of the two constituted the genetic material. Certain was only that the molecule had to be able to self-replicate and to mutate. Because proteins are so much more complex than nucleic acids, it was often assumed that proteins carried the genetic information. In 1928, Griffith [91] observed that avirulent Streptococcus pneumoniae became virulent in the presence of heat-killed virulent cells. Therefore, the genetic material seemed to remain active even though its host was dead. In 1944, Avery and colleagues [13] repeated Griffith’s transformation experiment but used DNA from virulent strains instead of whole heat-killed virulent cells and again obtained the virulent form of S. pneumoniae from avirulent bacteria. This established that Griffith’s transforming principle was DNA. Nine years later Watson and Crick [256] published the structure of Bform DNA. The excitement about this structure was great because it fitted so well the expectations of what a gene should be: a molecule capable of instructing its own replication. Today, we may think of a gene as a fragment of DNA that encodes a cellular molecule, protein or RNA. In procaryotes this usually includes the promoter region, which binds the RNA polymerase and is located at the 5’ end of the gene. Eucaryotic genes may additionally be characterized by an intron/exon structure and signal sequences located at the 3’ end of the gene or in introns. However, promoters and other signal sequences often remain unannotated. For example, Figure 7.1 shows the GenBank entry with accession number (ACCESSION) X78384 for the Adh gene

118

7 Gene Prediction

of Drosophila melanogaster and its duplicate, the Adhdup gene. The entry contains under the heading FEATURES annotations for introns, exons, the mRNA, and the protein coding sequence (CDS) but none for the promoter. Figure 7.2 gives a graphical representation of these features.

7.2 Computational Gene Finding One aim of genome sequencing projects is to compile a catalog of all genes in a genome and there is a lot of interest in automating this process, called automatic sequence annotation. A number of programs have been published and are routinely used for this purpose. Initially, programs were developed for procaryote sequences. In this case, gene finding by computational methods turned out to be relatively easy, because procaryotes have a high gene density and genes—with a few exceptions—do not have introns. For eucaryotes, the gene finding problem proved to be much more complicated. In the years preceding the publication of the human genome, the total number of human genes was a hotly debated subject and bets on the size of the gene complement could be placed at a public website. Those numbers ranged from 20,000 to 200,000. When the two draft human genome sequences were published, the total gene number was estimated to be 31,000 [128] and 26,000 [252] genes in these papers. This is far fewer than previously expected by most scientists, but very close to the rough estimates from the 1950s [102] and 1960s [145], which were motivated by population genetic arguments. Below we explain a few general principles of gene finding in eucaryotes and discuss why it still remains a difficult problem to exactly determine the number, location, and structure of genes in a genome. The first programs for automatized gene prediction were created in the early 1980s [75]. Subsequently, gene prediction became a rapidly advancing field within bioinformatics. Today, different methods for this purpose are implemented in a large number of programs. Two classes of gene prediction methods can be distinguished, ab initio and comparative, sometimes also referred to as intrinsic and extrinsic (Fig. 7.3). Ab initio gene prediction refers to those programs which search a single query sequence for certain signals—for example splice site signals—and compositional features, from which location and exon-intron structure of hypothetical genes are deduced. Comparative methods, on the other hand, build their gene predictions upon similarity of the query with other DNA or protein sequences and the differing degrees of similarity in functional and non-functional regions of the genome. Similarity is determined by an alignment procedure. Many of the programs which are in use for large scale genome annotation are hybrids of the two methods. Modern gene prediction programs can detect genes on either strand, forward or reverse, of a given query sequence (Fig. 7.4). All gene prediction programs aim to decipher the exon-intron structure, i.e the exact location of splice sites, and translation start and stop codons of the coding

intron

exon

intron

CDS

exon

intron

exon

mRNA

gene

JOURNAL MEDLINE PUBMED COMMENT FEATURES source

REFERENCE AUTHORS TITLE

LOCUS DEFINITION ACCESSION VERSION KEYWORDS SOURCE ORGANISM

aattaggtga aagttttatg tttgtatgca cctatattca ttttaagtaa atatattttg ttttaaagca taatcttgtg .......... gatctgaagg gtattcaatc agctgaaggc ccattgccga tcctgatcaa actacactgg gcggtcccgg aggtgcccgt taagttgatc cacctttaga acccgcacca gagaagctcc gcagttcagc

tagaacttgt caaattgaaa aaaaatacat aaatttgctc ttaagtggag aataaataaa ttatagtatt tgcgagcatg .......... taactatgcg ctagaacctg aatcaatcca gaccaccaag cggagctggt cctggtcaac tggtatcatc ctactccggc aaaggaaacg aactggcccc ccctggtgca tggctcatcc agacgggcta

gtgcacacac gcttatttct atgaatttgc aagaaaatat taaacatata taaacaaata gccgatttaa ggttaaatct .......... atgcccacag gtgatcctcg aaggtgaccg ctgctgaaga atcctggacg accacgacgg tgcaacattg accaaggccg caaagttttc cattaccggc caagttcaac cacccagcca acgagtactt

acatatagtt tccgcatgct agtagcctcc ttgaaccaaa caattttatt tatataaaat ttaatataat agctgcattc .......... gctccatgca accgcattga tcaccttcta ccatcttcgc atcaccagat ccattctgga gatccgtcac ccgtggtcaa aagaaaaaac gtgaccgctt tcctggttgg tcgttggcct gcatctcttc

ctatatcaac tatctctttc tcccacatca ttgattttta cttaccaaac ctacgaaatt taaataatat gaaaccgcta .......... gcgatggagg gaacccggct cccctatgat ccagctgaag cgagcgcacc cttctgggac tggattcaat cttcaccagc aaaactattt acaccgtgaa atgttgagcc gcgccgagaa ..........

/number=3 2660..3100 /gene="Adh" /number=4 3226..4761 /gene="Adh-dup" join(TAA TAC --->TAG TAT TCA TCC TCG TCT --->TGA TGC TGG TGT TTA TTC TTG TTT

Fig. 7.7. Differential codon usage in human, mouse, and Drosophila. Vertical axis: 64 codons; horizontal axis: , where and are the relative codon frequencies for a given amino acid in human/mouse and human/Drosophila comparisons. Non-degenerate codons are necessarily unbiased, for instance the codon ATG.

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7 Gene Prediction

Table 7.4. Alignment of eight nonamers comprising the splice donors from two genomic sequences of D. melanogaster, GenBank entries U04239 and M61127. The donor dinucleotide is displayed in bold. Nucleotides within exons are capitalized. fragment f f f f f f f f

accession number U04239 U04239 U04239 U04239 U04239 U04239 M61127 M61127

A C G T

-3 2/8 2/8 1/8 3/8

-2 3/8 0/8 4/8 1/8

5’ end of intron donor 1 donor 2 donor 3 donor 4 donor 5 donor 6 donor 1 donor 2

-1 1/8 0/8 7/8 0/8

0 0/8 0/8 8/8 0/8

1 0/8 0/8 0/8 8/8

2 5/8 0/8 2/8 1/8

position 868-876 992-1000 1481-1489 2356-2364 2669-2677 3291-3299 226-234 539-547

3 7/8 0/8 0/8 1/8

4 0/8 1/8 7/8 0/8

sequence CTGgtgagt GGGgtaagt CGGgtaagt AGGgtaagt AAAgtaagt TAGgtaact TGGgtttgt TAGgtgagt

5 0/8 0/8 0/8 8/8

Fig. 7.8. Frequency matrix of each nucleotide at each of the nine positions in the alignment in Table 7.4. The positions of the donor dinucleotide are set to and .

How do the observed nucleotide frequencies from donor sites compare to nucleotide frequencies from random nonamers? To simplify matters, let us assume that the four nucleotides occur with equal frequencies in the genome of D. melanogaster. We then determine the log-likelihood of each nucleotide at each position by taking , and obthe logarithm of the ratio of observed and expected frequencies, tain the log-likelihood, or position weight matrix (PWM), shown in Figure 7.9. The PWM is said to be of Markov order , if the frequencies at all sites are evaluated independently, as done here. For instance, consider the entry 0.58 at position 2 for nucleotide A. It is computed as

As described in Chapter 6, a query sequence can be scanned with a PWM profile can be assigned to each position of the query. High scoring donor and a score candidates are those positions where the score exceeds some (positive) threshold. When choosing the logarithm with base two, as done here, the log-likelihoods have an information-theoretical interpretation. Consider a set of random oligomers of a fixed length, say nine. The “uncertainty” [224] at each site in this set of oligomers is given by

7.4 Ab initio Methods: Searching for Signals and Content -3

-2

-1

0

1

2

3

4

129

5

A C G T Fig. 7.9. Position weight matrix for the primitive donor finder (see text).

where is the relative frequency of nucleotide at position . When the back, uncertainty is . ground nucleotide frequencies are all equal, The “information content” of a site in a profile such as Figure 7.9 is the “decrease , and is given by in uncertainty”,

For the nine positions in the donor profile we derive the information content given in Table 7.5. Table 7.5. Information content at the individual sites in the donor profile from Figure 7.9. For in order to numerical evaluation of the information content we take avoid undefined values when the relative frequency is equal to . position

information content

The information content of a profile can be visualized graphically as a so-called sequence logo [219]. Figure 7.10A displays the sequence logo for the donor profile of the primitive donor finder. At each position letters A, C, G, T are stacked on top of each other. The height of a letter, , is given by the product ; the combined height is then equal to the information content of . We now use the primitive donor finder to search for putative donor sites in a genomic sequence. As a test sequence we choose the Adh locus from D. melanogaster (cf. Figure 7.2). We find three putative donor sites, indicated by arrows in Figure 7.11. The donor scores in Figure 7.11 are simply the sum of the respective entries in the profile weight matrix. Only donors with positive scores are shown. By

7 Gene Prediction A

G A T C

T C A

T A

G

9

T C

10

G

3

2

1

T

8

AA

G

7

GT

C

-1

-2

3′

0 5′

T

T C

6

G

5

G AA

TC

-3

T

-4

G

-5

T

1

A

AA

-3 -2 -1 0 1 2 3 4 5

0 5′

T

GGT AGT

-6

G

-7

1

exon | intron

2

0

exon | intron bits

bits

2

B

4

130

3′

position

position

Fig. 7.10. A: Sequence logo of the eight donor sequences used for the primitive donor finder shown in Table 7.4. B: Sequence logo built from 757 Drosophila donors available at http: //www.fruitfly.org/GASP1/data/data.html. -axis: sequence position around donor dinucleotide GT (at positions 0 and 1); -axis: information content at each position in bits. Note that in B the number of informative positions around a Drosophila donor site is limited to about five nucleotides downstream of GT and two nucleotides upstream.

1

1000

2000

3000

4000

5000

Fig. 7.11. Applying the primitive donor finder PDF to GenBank entry X78384. The vertical arrows indicate positions of putative donors with a positive score.

comparing this result to the mRNA feature in the corresponding GenBank entry in Figure 7.1, we see that the first two reported donor sites are true donors of the Adh gene and that the third predicted donor site is a false positive: it does not correspond to any of the annotated donor sites. Furthermore, the PDF tool fails to recover the third donor of the Adh gene and both donors of the Adhdup gene (cf. the annotation in Figure 7.1). Thus, there are three false negative predictions, and the PDF has a sensitivity of

and a specificity of

Clearly, these values are not satisfactory. A strategy to improve the low accuracy of our PDF tool would be to compile a much larger training set of verified donors in order to build a better tuned position weight matrix. One such set, for instance, has been compiled during the GASP [206] experiment and can be accessed through their website. The sequence logo obtained with this set is displayed in Figure 7.10B. There are additional parameters which should be optimized. For instance, the score

7.4 Ab initio Methods: Searching for Signals and Content

131

threshold below which a putative donor site is discarded, may be chosen to be different from zero, as was done here. The optimization criterion is to maximize on a , for well-defined training set some function of sensitivity and specificity, . instance the average 7.4.3 Exon Chaining In a similar manner to the donor search, genomic sequences may be scanned with the help of position weight matrices for acceptor sites, translation start, and stop signals. These scans can be combined to yield candidate exons of the four types: single, initial, internal, and terminal. The next step in predicting gene models with multiple exons is to select suitable exons from the lists of candidates. This problem can be solved by chaining of compatible exons [95]. Consider for example the “exons” shown in Figure 7.12. Each exon has a score, indicated by its position along the -

4 3 Score

7 6

1 0

8 5 9

2

Position Fig. 7.12. The chaining problem consists of finding a path of non-intersecting intervals such that the combined score of the intervals which are included in the path is maximized (bold lines).

axis, and an identifier, indicated by the number written on top. What we are looking for is the combination of non-overlapping exon candidates that yield the maximum score. In our example this combination happens to consist of intervals 9, 7, 3, and 0, which are shown in bold; their combined score is 31. The chaining algorithm shown in Figure 7.13 can be used to find such an optimal combination of exon candidates. Chaining is based on dynamic programming and starts from a list of exon candidates, . The algorithm consists of the standard two phases of dynamic programming: (i) a forward phase in which a table of size is filled in to find the score of the optimal solution and (ii) a traceback phase in which the intervals belonging to the optimal solution are retrieved.

132

7 Gene Prediction

Require: //array of intervals Require: //sorted array of the //Forward Algorithm for all

for if

borders of the

intervals

do //initialize backpointers to “NULL” score of //initialize index to last element added to optimal chain to do is left border of interval then

else if

output //Traceback while output

then

do

Fig. 7.13. Exon chaining algorithm.

Apart from the list of exons, we also assume as given a corresponding list of exon borders, , sorted in ascending order. Each border contains a reference to its interval of origin. Table 7.6 shows the border table corresponding to our example set of exons. The algorithm starts by initializing four variables: 1. : the current score of the growing chain is set to zero; 2. : the array of cumulative scores is set to the original interval scores; 3. : the array of back pointer indices to the previous element in the chain is set to “NULL”; 4. : the index of the last element added to the chain is also set to “NULL”. Figure 7.14A shows the initialized variable . After initialization, the algorithm proceeds along the list of borders starting at the left-most element. Say, the algorithm is currently dealing with border , which refers to some interval . If is a left border of interval , two operations are carried out: 1. 2.

is set to the score that the chain would have, if was added to it; and is set to the last element that was added to the chain.

If, on the other hand, is a right border, we need to decide whether or not to add to the chain. Addition is executed if the score of the chain including , , is greater than , the score of the current chain. In that case is updated to the new score and to the index of the current exon, . If is not added to the chain, nothing is done. Figure 7.14B shows and the back pointers at the end of the forward phase of the

7.4 Ab initio Methods: Searching for Signals and Content

133

algorithm. Next, the traceback starts from , which in our case is 9. By following the back pointers, we get the exons shown in bold in Figure 7.12. Table 7.6. Sorted list of borders of the intervals shown in Figure 7.12. # Position Interval Side

# Position Interval Side

0 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19

1 2 2 2 3 4 7 9 10 11

0 2 1 0 3 4 5 6 3 1

l l l r l l l l r r

12 12 13 13 14 15 15 16 17 19

7 6 8 2 7 9 8 9 5 4

l r l r r l r r r r

The chaining algorithm as described so far still needs to be refined by incorporating more of the biological reality it is designed to model. For a start, only those exons which do not disrupt the reading frame can be included in a chain; furthermore, it is attempted to build complete chains, which consist of exactly one initial exon, an arbitrary number of internal exons, and one terminal exon. This procedure resembles very much the way in which tiles in a domino game are laid out. A 0 1 2 3 4 5 6 7 8 9

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Fig. 7.14. Scores and back pointers for chaining the exons shown in Figure 7.12. A: Initialized array; B: filled in array.

In practice, before the chaining step is performed, additional requirements are imposed to reduce the number of candidate exons. For instance, a coding exon cannot contain internal stop codons and its length must fall within some pre-set range.

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Furthermore, the distance between neighboring exons in multi-exon genes may not exceed an upper threshold. On the other hand, such requirements may themselves become a source of errors. For example, the intron length distribution is highly variable among species and giant introns are known to exist in most vertebrate genomes. Given a conservative threshold regime, very long introns might be missed, possibly resulting in the erroneous prediction of split genes. In addition to these structural properties, measures of sequence composition are also used to better discriminate between coding and non-coding sequences. One such measure is codon bias. Furthermore, neighboring nucleotides are not independent of each other. Therefore, nucleotide composition, in particular of coding regions, is not adequately described by a Markov model of order zero. Experiments have shown that it is a good strategy to consider instead the hexamer composition of a sequence. A -mers. Hence, Markov model of order accounts for dependencies at the level of a fifth order Markov model is sensitive to dependencies at the level of hexamers. In a homogeneous model all positions are treated equally. In a non-homogeneous model one can, for instance, account for the triplet structure of typical coding regions. The gene prediction program GenScan [30] uses a homogeneous fifth order Markov model for non-coding regions and a three-periodic fifth order Markov model for coding regions. The parameters for this model are obtained from a training set with known coding and non-coding regions. The switching between the coding and the non-coding state is what makes the model a hidden Markov model. Hidden, because only the emitted nucleotide sequences are directly observable, while the underlying state (intron, exon, or intergenic) of the model remains hidden. GenScan switches between states according to a Markov chain, but in addition the duration of each state is influenced by empirical probability distributions of intron and exon lengths.

7.5 Comparative Methods 7.5.1 General Remarks Instead of relying only on sequence signals and compositional features deduced from the query sequence itself, comparative methods also use information obtained from additional, homologous sequences. There are three types to be distinguished: (i) methods in which a genomic sequence is compared with homologous proteins from the same or different species, (ii) methods in which a genomic sequence is compared with a collection of expressed sequence tags (ESTs) or cDNAs, and (iii) methods, where two or more genomic sequences are compared with each other. Examples of gene finding programs of the first type are procrustes [85] and genewise [22]. In the next section we discuss two example programs which are representative for the second and third type. In all cases a so-called spliced alignment (Fig. 7.15) of query and subject sequence highlights those fragments which are identical or highly similar. Dedicated programs to compute spliced alignments, such as sim4 [79] or est genome [185], can accommodate large gaps, which may correspond to putative introns. This is only possible if gap costs remain bounded, even if the gap

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Fig. 7.15. Types of spliced alignments. Top: genomic query and protein, cDNA or EST subject sequence; bottom: genomic query and homologous genomic subject sequence.

size becomes large. Simple affine linear gap functions, described in Chapter 2, are not suited for this task, but need to be adapted accordingly. At the same time, these programs also attempt to identify potential splice junctions and to insert gaps preferentially at those junctions. When two (or more) genomic sequences are compared, a strategy to search for exons and splice junctions is to inspect the pattern of sequence similarity in an alignment and the transitions between highly conserved and diverged regions. The underlying idea is that coding sequences are more conserved between species than noncoding sequences. Therefore, the variation in sequence similarity can be used as a quite reliable indicator of the location of protein-coding genes and of the boundaries of (coding) exons and introns. Besides alignments of orthologous sequences (from different species), alignments of paralogous sequences (from the same species) have also been used for this purpose. A disadvantage of these programs is that they have difficulty in locating untranslated exons. Although sequence similarity between orthologous untranslated exons tends to be higher than in introns, it is generally not sufficient to clearly delineate the exact boundaries of the 5’ and 3’ untranslated regions of genes. In principle, untranslated exons and their boundaries can be inferred with the help of ESTs, since they are derived from mRNA. However, the drawback here is that the necessary ESTs may be absent from the EST database. 7.5.2 Comparative Gene Prediction at the Adh Locus As an example for a comparative gene prediction tool, we apply GeneSeqer [250] to our test sequence with the two genes Adh and Adhdup from D. melanogaster (Fig. 7.2). GeneSeqer computes spliced alignments of the query and all matching ESTs, and returns putative gene locations, as well as explicit gene structure and protein predictions. A GeneSeqer prediction is based on the consensus of a set of clustered, or aggregated, ESTs (Fig. 7.16A). In contrast, the result of a simple BLAST search in the D. melanogaster-EST database is shown in Figure 7.16B. Unfortunately, at the time of writing, no Drosophila ESTs were available which cover the Adhdup region (position 3226 to 4761). Therefore, any EST-driven gene finder would miss this gene (see Figure 7.16A).

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Fig. 7.16. Gene prediction at the Adh locus of D. melanogaster. A: Known gene structure (GenBank entries X78384 and M17827). B: Graphical output of GeneSeqer [250]; PGL—“putative gene location”, PPS—“predicted protein sequence”, PGS—“predicted gene sequence”, AGS—“aggregate gene sequence”. C: Output of blastn when searching the D. melanogaster EST database at NCBI (posted date September 27, 2005).

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In Figure 7.17 the gene structures predicted by GeneSeqer are compared with the ones calculated by Slam. The latter is based on a pairwise alignment between two genomic sequences. Here, orthologous sequences of D. melanogaster and D. simulans were compared. The two fly species diverged about 1-2 million years ago. Slam yields a perfect prediction of the coding part of the D. melanogaster Adh gene and simultaneously also retrieves the Adh gene in D. simulans. Furthermore, Slam detects part of the Adhdup gene. However, it predicts here a split gene, and only the terminal exon of the Adhdup coding sequence is correctly predicted. Similarity-based gene prediction programs tend to have a high false positive rate when divergence between the two compared sequences is low. This is the case for the two Drosophila sequences (see Figure 7.18). Furthermore, note that GeneSeqer is able to correctly identify the 3’ untranslated part of the Adh gene and nearly correctly the 5’ untranslated part, since ESTsupport is available. In contrast, Slam was not able to find untranslated regions. GeneSeqer identifies the internal coding exon, but it does not recognize the correct start and stop codons of the Adh gene. It even splits its prediction into two different genes (see Figure 7.17). Furthermore, it does not recognize intron 2, but joins exons 2 and 3 into a single exon. Adhdup is completely missed by GenSeqer. Finally, note that the EST cluster at the Adh locus contains ESTs which support different splice forms. One reason for this is that ESTs usually do not represent the complete transcript (so-called full length cDNA), but only part of it. Sometimes they are contaminated with foreign DNA, for instance vector sequences. A much more important reason, however, is that different ESTs may represent transcript variants and thus be indicative of alternative splicing in this gene. In this case, alternative transcripts are indeed known to exist [152]. Kreitman has described an adult and a larval form of Adh (GenBank entry M17827). Nonetheless, none of the currently available ESTs supports the known larval splice variant and it is therefore missed by GeneSeqer (see Figure 7.16A and 7.16B). 1

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Fig. 7.18. Alignment of two genomic sequences from D. melanogaster and D. simulans. Upper panel: -axis—D. melanogaster sequence (4,761 bp), -axis—D.simulans sequence (4,607 bp). Lines represent locally aligned segments. Note that the entire region is highly conserved. Dark boxes (along the border) correspond to the Slam predicted exons, light boxes represent the annotated exons (only CDS) of the Adh and Adhdup genes. Lower panel: Identity values of the aligned segments. Note that traces of the gene duplication are still visible: there are short fragments in Adh and Adhdup with an identity of about 55%. Since the duplicated fragments occur in both sequences, the duplication event must predate the speciation event leading to D. simulans and D. melanogaster.

7.6 Problems and Perspectives Despite the success of automatic gene prediction in whole genome annotation projects (genome.ucsc.edu, www.ensembl.org), a number of problems remain. Some of them were encountered in the above example. Many programs only predict protein-coding sequences and ignore untranslated regions and regulatory elements of putative genes and non-protein-coding genes altogether. Long introns often lead to erroneous splitting of genes. On the other hand, in regions of high gene den-

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sity, programs tend to join distinct genes. On a genomic scale it is difficult for gene prediction programs, which are typically trained on a limited set of verified genes, to deal with fluctuating gene densities along a genome. Furthermore, program accuracy is species-specific. This is especially true for all content-based programs, which work with species-specific codon usage tables. Finally, alternative splicing plays an important role in many eucaryotic genes. However, to predict possible transcript variants and to control at the same time the number of combinatorially possible variants turned out to be an extremely difficult problem. Comparative methods come with their own set of advantages and disadvantages. EST-based gene prediction depends on the availability of a comprehensive EST database. However, it is difficult to assess the degree of completeness of an EST database. Inter-specific as well as intra-specific genomic comparisons require at least two homologous sequences at the appropriate evolutionary distance. Such sequences may not always be available. Much development effort is spent on combining the advantages of the different gene prediction—or genome annotation—strategies. Developers aim to raise the level of prediction accuracy. However, the exact number of genes in the human or any other complex genome may well turn out to be fundamentally uncertain. Many genes may not have the well-defined “true” structure, which is assumed to exist when evaluating the accuracy of gene predictions. Alternative splicing, differential expression, and interaction with other genes within a complex network make genes much less clear-cut objects than their underlying sequences.

7.7 Summary Gene prediction is concerned with the automatic detection of genes in nucleotide sequences. Genes consist of a number of features such as exons, introns, promoters, coding sequence, untranslated regions, and transcriptional regulators. These differ widely in the accuracy with which they can be predicted. The accuracy of a given method of gene prediction is quantified through sensitivity and specificity. Sensitivity is the number of correctly detected features divided by the total number of detectable features. Specificity is the number of correctly detected features divided by the number of features returned by a detection method. An ideal gene prediction algorithm is characterized by a sensitivity and a specificity of one. There are two classes of methods for gene finding: ab initio and comparative. Ab initio methods detect signals contained in the DNA sequence in an attempt to emulate the transcription and translation machinery of a cell. Comparative methods are based on alignments of more than one input sequence. In such alignments functional regions such as exons stand out as islands of elevated sequence conservation.

7.8 Further Reading Reviews of gene prediction have been published by, among others, Guigó [94], Burge and Karlin [31], and Zhang [264]. An excellent online bibliography on

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the topic has been compiled by Wentian Li and can be found at http://www. nslij-genetics.org/gene/.

7.9 Exercises 7.1. The coordinates of the three exons that make up the CDS of Adh in D. melanogaster are 2021–2119, 2185–2589, and 2660–2926. What is the reading frame of these three exons? 7.2. Determine a recursive formula, analogous to Equation (7.2), to compute the remainder of exons. 7.3. Determine a formula to convert frame into remainder and vice versa. 7.4. Given a uniform distribution of the four nucleotides, what is the number of expected acceptor (donor) dinucleotides in a random sequence of length 100 kb? 7.5. How many stop codons do you expect to find in an exon of length 100 bp in any of the three reading frames on both strands (a) if the G/C-content is equal to 0.5, (b) if the G/C-content is equal to 0.3, (c) if the G/C-content is equal to 0.6? 7.6. What is the average distance between an acceptor and a donor site in a random sequence? Are these distances different in an infinitely long and a finite sequence, say, of length 100 bp (consider here only non-nested exons)? 7.7. Given the log-likelihood matrix in Figure 7.9, what is the expected number of ) in a random sequence (all four nucleotides donor sites with positive score ( have equal probability) of length 100 kb? Compare this number to the number of expected GT dinucleotides. 7.8. Download the set of verified donor sequences listed in Figure 7.10B. 1. Build a frequency matrix and a log-likelihood matrix (PWM) comprising 10 positions (3 upstream + donor dinucleotide + 5 downstream). 2. Determine the information content of each position of this profile.

Part II Sequences in Time

sdfsdf

8 Phylogeny If evolution is true, why are there still monkeys? Larry King [146]

Biological sequences are the product of evolutionary history and phylogenies are graphical summaries of this history. The simplest and most widely applied model of evolution is a bifurcating tree. As explained previously (Section 3.4), a tree consists of nodes and edges connecting the nodes. The leaves in a phylogenetic tree represent extant sequences of the corresponding organisms. Internal nodes represent common ancestors of all sequences in the respective subtree. In a bifurcating tree each internal node leads to exactly two child nodes. The distances along edges are often interpreted as proportional to time and an example of such a tree is shown in Figure 8.1A, which has been calculated from the globin multiple alignment of Figure 5.1. As for every phylogenetic tree, there are two important aspects to the globin tree in Figure 8.1A: (i) the topology or branching order of the tree, and (ii) its branch lengths. Consider topology first. The globin tree implies that the -chain of hemoglobin ( -globin) from human and horse form the most closely related pair of sequences. The next most closely related pair of sequences consists of the -globins of the same taxa. Together the - and -globins form a mammalian globin cluster. The closest relative of this cluster is globin 5 from lamprey, followed by sperm whale myoglobin, and finally leghemoglobin from yellow lupine as the sequence that is the most basal member of the globin protein family. We can interpret branch lengths in Figure 8.1A as proportional to time. If we knew the mutation rate of globins, we could then calculate the times for each of the internal nodes in the tree. The implicit assumption we are making here is that the mutation rate is equal across the branches. This assumption is known among biologists as the molecular clock [28], which is further discussed in Chapter 9. Certain phylogenetic algorithms are based on the assumption of a molecular clock. The vertical line in Figure 8.1A emphasizes that all branches in the corresponding tree end at the same point in time, the present. Other methods do not assume a molecular clock and an example of such a tree for our globin data is shown in Figure 8.1B. Here the interpretation of branch lengths as proportional to time raises the apparent paradox that not all leaves of the tree are aligned along the present. Has globin 5 from lamprey, for example, not yet reached the present? It obviously has and we need to read Figure 8.1B as a summary of the number of mutations estimated to have

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Fig. 8.1. A: Rooted phylogenetic tree of seven globin sequences calculated using the UPGMA algorithm that assumes a molecular clock. The thin vertical line indicates the present point in time. B: Midpoint rooted phylogenetic tree of the same globin sequences calculated using the neighbor-joining algorithm that does not assume a molecular clock. C: Unrooted version of B. D: Outgroup rooted version of B. M: position of midpoint root; O: position of outgroup root.

occurred between each pair of sequences. Figure 8.1B therefore tells us that in the past the rate of mutation was not identical along all the branches of the topology and in a biological setting one would often go on to test whether these deviations from rate equality were significant [186]. Suffice it to say here that of the algorithms used to reconstruct phylogenies some are based on the assumption of a molecular clock, while others are not. We will make these differences more precise below. As important as the distinction between algorithms with and without molecular clock is the dichotomy between methods that return rooted and those that return unrooted phylogenies. Formally, this distinction refers simply to the fact that a rooted tree contains a single node (the root) that is not connected to a parent node, while an unrooted tree contains no such node (Fig. 8.2). In the absence of a molecular clock, the resulting tree is initially unrooted and Figure 8.1C shows the unrooted version of Figure 8.1B. This unrooted tree implies no particular direction for evolutionary time. In other words, Figure 8.1C gives no indication as to the position of the last common ancestor of the clade depicted. There are two widely used methods of determining the position of the root in this tree: midpoint rooting [67] and the more commonly applied rooting by outgroup. When

8.1 Is There a Tree?—Statistical Geometry rooted

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Fig. 8.2. The smallest rooted and unrooted phylogenies.

using midpoint rooting, the root is placed in the middle between the two most divergent taxa, in our case lupine leghemoglobin and horse -globin. This position is marked as M in the unrooted tree (Fig. 8.1C). When the tree is rooted at this point, we get the topology shown in Figure 8.1B. This tree implies a constant rate of evolution between the two most divergent taxa and accordingly horse -globin and lupine leghemoglobin are both flush at the imaginary present line. Alternatively, we may note that the most recent common ancestor of leghemoglobin and the other six globins must have been the most recent common ancestor of all our seven example globins. Biologists then speak of leghemoglobin as being the outgroup with respect to the remaining six globin sequences. An outgroup can be used to assign a “pseudo root” to a tree. This is done by rearranging the tree around the node O in Figure 8.1C, which is the node at which the outgroup joins the tree. The result of this rearrangement of branches is shown in Figure 8.1D. Notice that node O is structurally the same as all other internal nodes in the tree as it is connected to three other nodes. Recall that in a bifurcating phylogeny a true root is connected to only two other nodes (Fig. 8.2), hence our designation “pseudo root”. We have already seen that we might not be able to model evolution by a constantrate mutation process, which results in an unrooted tree possibly with uneven branch lengths. Perhaps more worryingly, there might not even be a tree. In the subsequent sections we start by investigating whether there is a tree to be discovered in the first place. This is followed by a description of three widely used classes of methods for phylogeny reconstruction: (i) distance-based methods, which are also used to construct guide trees in progressive multiple sequence alignment algorithms (cf. Section 5.3), (ii) maximum parsimony, and (iii) maximum likelihood. We finish the chapter explaining how to assess the robustness of the clusters in our phylogenetic tree through a simple but powerful resampling technique known as the bootstrap [56, 71]. Readers interested in more details may wish to consult the comprehensive monograph on phylogenetic reconstruction by Felsenstein [73].

8.1 Is There a Tree?—Statistical Geometry Statistical geometry is a method for assessing the phylogenetic signal contained in a multiple sequence alignment. It is easiest to explain for binary sequences and we concentrate on this case here. You might wonder what relevance binary sequences

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might possibly have for biological sequences, given that DNA is an alphabet over four bases, not two. However, the bases can be recoded into purines and pyrimidines yielding binary sequences. Consider, for example, the hypothetical quartet of aligned sequences shown in Figure 8.3A. There are eight possible configurations for each column and an example for each is given in Figure 8.3A: either all four nucleotides are identical, in which case the position is designated as “0” and ignored in the subsequent analysis; or one nucleotide is different from the other three, in which case there are four ways of obtaining this configuration, designated , , , and ; finally, the quartet might divide into two pairs of equal nucleotides, in which case there are three possibilities, designated , , and (Fig. 8.3A). Having thus classified each position for a quartet of sequences, the next step in statistical geometry is to count the number of occurrences of each configuration. The result is a three-dimensional diagram, which correctly depicts the mutational distance (of four) between all four sequences (Fig. 8.3B). If more than four sequences are analyzed, possible quartets that can be formed from sequences are investigated and all averages calculated for the seven parameters of interest, hence the name statistical geometry. A YRYYYYRR YYRYYYYY YYYRYRRY YYYYRRYR Class 0ABCDXYZ

B B C Y D Z X

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Fig. 8.3. Statistical geometry. A: Alignment of four sequences consisting of purines (R) and pyrimidines (Y) and the class of sequence configuration each column is assigned to. B: Depiction of the classes detected in A. Box dimensions are marked by bold lines and labels.

The method allows us to distinguish three principal topologies that might describe the data from which phylogenetic reconstruction is to be attempted: the tree, the net, and the bush. A bush is obtained if all three of the box dimensions of the net shown in Figure 8.3 are zero, collapsing the box into a point from which the taxa emerge forming what is known as a “star” phylogeny (Fig. 8.4). If we start from a bush and expand only one of the three box dimensions, we obtain the three possible tree topologies (Fig. 8.4). If we expand more than one box dimension, we obtain the four possible net topologies (Fig. 8.4). Since its inception [60], statistical geometry has been further developed into likelihood-mapping which is explained next.

8.2 Likelihood-Mapping We start again with the simplest case of phylogenetic reconstruction and consider . Any given quartet of taxa can be connected by three a quartet of taxa,

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different unrooted trees (Fig. 8.5). The likelihood of a tree, , is the probability . For a set of sequences of observing the input data, , given the tree: the likelihood attached to each of the three possible trees is determined for each of the possible quartets. We are at this point not concerned with the computation of the likelihoods and regard them simply as scores for each of the three trees. Each likelihood score can be normalized by division by the sum of all three likelihoods, resulting in three numbers ranging between 0 and 1. These can be plotted into a triangular coordinate system, where each vertex corresponds to one of the three trees quartets that [237]. Such a plot is shown at the top of Figure 8.6 for the can be formed from our seven globin sequences. If all quartets supported only one tree each, then all 35 points in the diagram would lie on the vertexes of the triangle. However, as displayed in the bottom right triangle in Figure 8.6, this is only true of the quartets. This indicates that overall for there is a strong phylogenetic signal in the data, but some parts of the tree will not be perfectly resolved. Before we proceed to reconstruct a phylogeny, let us consider how many possible topologies there are to choose from.

Fig. 8.5. Quartet analysis. Top: A tree consisting of the seven taxa can be divided into quartets of taxa. An example quartet is shown in bold. Bottom: the three possible topologies of the example quartet; the tree in the middle corresponds to the quartet marked bold in the top tree.

8.3 The Number of Possible Phylogenies The simplest unrooted tree consists of three taxa and has a single possible topology (Fig. 8.2). The simplest unrooted tree with more than one topology connects four

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Fig. 8.6. Likelihood-mapping [237] of the phylogenetic content of the globin multiple sequence alignment shown in Figure 5.1. The likelihood-mapping was carried out using the program TREE-PUZZLE [218].

taxa and there are three possible configurations (Fig. 8.5). If we add a fifth taxon to our tree, this can be joined to each of the five branches in each of the three trees, i.e. possible unrooted trees for five taxa. In general there are there are

possible unrooted trees. The simplest rooted tree consists of two taxa and it has only a single topology (Fig. 8.2). The simplest rooted tree with more than a single possible topology connects three taxa and there are three possible configurations (Fig. 8.7). In order to un-

Fig. 8.7. The three possible rooted phylogenies connecting taxa

derstand how many rooted trees there are for

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an unrooted tree is converted into its rooted version. This is done by splitting any of the edges and connecting the free ends of the edges to the root (Fig. 8.8). An unedges. Since any of the edges can receive the root, the rooted tree contains number of rooted phylogenies is

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is a simple function of the number of unrooted trees,

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Fig. 8.8. Rooting a phylogeny. The unrooted phylogeny on the left is rooted at the position indicated by the arrow. The resulting rooted phylogeny is shown on the right. The root can be placed on any of the five edges of the unrooted phylogeny.

For merely 20 taxa there are unrooted and rooted phylogenies. This raises the question of how to choose efficiently between the vast number of alternative phylogenies. There are two general approaches to this problem: (i) to search tree space or (ii) to use an algorithm that avoids this. Searching tree space necessitates a score function for the trees visited, and either the length of the tree in terms of implied mutational steps (maximum parsimony, Section 8.5) or the likelihood of the tree given the data (maximum likelihood, Section 8.6) are used for this purpose [238]. On the other hand, distance methods of phylogenetic reconstruction pairwise distances between (Section 8.4) directly calculate a phylogeny based on taxa.

8.4 Distance Methods Before we can apply distance methods, we need to be able to measure the distances between sequences. A na¨ıve method for calculating distances between aligned sequences is to count the pairwise mismatches. However, this underestimates the true number of substitution events per site, as a site might change more than once in the course of evolutionary history. The simplest model to correct for this effect is known

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as the Jukes-Cantor model [130]. As shown in Section 9.5, the number of substitu, can be estimated under the Jukes-Cantor model from tions per site over time , pairwise sequence alignments using the formula

where is the probability that two homologous nucleotides differ after divergence time . This quantity is estimated from a multiple alignment by counting the number of pairwise mismatches and dividing by the total number of residues considered. Figure 8.9 shows an alignment of five primate nucleotide sequences. This contains 16 polymorphic positions, all of which have only two nucleotide states, that is, are bi-allelic. The number of pairwise differences and the corresponding JukesCantor distances are displayed in Figure 8.10.

Human Chimpanzee Gorilla Orangutan Gibbon

GTAAATATAGTTTAACCAAAACATCAGATTGTGAA .....t.........c........c.......... .....t.........c........c.......... .....t.........c........t.......... .....c.........t........t..........

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TCTGACAACAGAGGCTTACGACCCCTTATTTACCG ...g.c..c..a.g.tcacg...c...at...... ...g.t..c..a.g.tcaca...c...at...... ...a.t..t..g.c.ccaca...c...at...... ...a.c..t..a.g.tcgaa...t...gc......

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Fig. 8.9. Alignment of 70 nucleotides of mitochondrial DNA sequences from five primates containing 16 polymorphic sites. Dots indicate a match with the nucleotide in the top row; : homoplasy as explained in Section 8.5. Data taken from [111].

Distances that fit onto a tree with a molecular clock (cf. Fig. 8.1A) are known as ultrametric. Ultrametric distances obey the three point criterion, which states that , there are two equidistant pairs of taxa, with the distance for any three taxa between the third being less or equal to the other two [238]. Consider, for example, . the topology shown in Figure 8.11, where Distances that fit onto a tree without a molecular clock (cf. Figs. 8.1C and 8.1D) are called additive. Additive distances obey the four point criterion [238]. Consider . Between these four taxa six distances and three pairs of disfour taxa, tances can be formed. The four point criterion states that the sum of those pairs of distances that traverse the trunk of the tree are equal and greater, or equal to the sum of the remaining distances. The topology shown in Figure 8.12 gives an example of this. Ultrametric distances are additive, but not vice versa. The UPGMA tree shown

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Human Chimpanzee Gorilla Orangutan Gibbon 0.014 0.044 0.141 0.195 0.014 0.029 0.124 0.176 0.043 0.029 0.091 0.176 0.129 0.114 0.086 0.176 0.171 0.157 0.157 0.157 -

Fig. 8.10. Distance data computed from the alignment shown in Figure 8.9. The bottom triangle of the matrix contains the number of mismatches per position, the top triangle displays the corresponding Jukes-Cantor distances, that is, the estimated number of substitutions per position. The Jukes-Cantor distance is always greater or equal to the number of mismatches per position, as it corrects for the possibility of a mismatch corresponding to more than a single substitution event.

Fig. 8.11. Example of ultrametric distances, where

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in Figure 8.1A corresponds exactly to the distances in the input matrix if these are ultrametric. In contrast, the neighbor-joining tree in Figure 8.1B exactly reflects the distances in the input data if these are additive. The UPGMA algorithm is an example of average linkage clustering. This is explained next, followed by the neighbor-joining algorithm.

Fig. 8.12. Example of additive distances, where

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8.4.1 Average Linkage Clustering Average linkage clustering refers to a group of distance algorithms. The most widely used member of this group is the unweighted pair-group method using an arithmetic


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average (UPGMA) [232]. If our distance data is ultrametric, UPGMA will recover the correct tree. We start with only the leaves of our tree. The first internal node is found from the distance matrix by grouping those two taxa that have the smallest distance between them. These taxa are removed from the data set and replaced by cluster . The distance between and each of the subsumed leaves is just half the distance between the leaves. The distance between and some other cluster currently contained in the data set, , is the arithmetic average of the distances between members of and . Hence, the algorithm performs the following steps: between a set of taxa

1. Input is a matrix of pairwise distances 2. Find pair of taxa with smallest distance 3. Cluster taxa: Remove pair from set of taxa: Add new cluster to set of taxa: if , stop 4. Compute the distances between and all other taxa:

if the input data is ultrametric, this is always the same number 5. go to 2

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Figure 8.13 illustrates these steps. Notice that ultrametricity of the distance data implies that the number of distinct entries in the distance matrix must not exceed the number of internal nodes (including the root) in the tree. The number of internal and, hence, if a given distance matrix nodes in a rooted binary tree for taxa is contains more than distinct entries we know that the distances are not ultrametric. The distance matrix shown in Figure 8.10 is an example of this. It contains . We can still apply the UPGMA 8 distinct entries, which is greater than algorithm to this data and the result is shown in Figure 8.14. This tree agrees with

human chimpanzee gorilla orangutan gibbon Fig. 8.14. Primate phylogeny computed from the data shown in Figure 8.10 using the UPGMA algorithm.

the generally accepted primate phylogeny. However, in other cases a violation of the assumption of ultrametricity can lead to an incorrect phylogeny. Consider for example the phylogeny shown in Figure 8.15. The rates of evolution along the branches leading to taxa 2 and 3 are much lower than the rates along the branches leading to taxa 1 and 4. UPGMA would first cluster taxa 2 and 3 thus returning the wrong tree. In contrast, the neighbor-joining method uncovers the correct tree from the distance matrix shown in Figure 8.15.

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Fig. 8.15. A: Phylogeny with varying rates of evolution; B: corresponding distance matrix. Branch lengths are italicized. The neighbor-joining method can recover tree A from data B, while the UPGMA algorithm would erroneously cluster taxa 2 and 3.


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8.4.2 Neighbor-Joining Pairs of nodes in a phylogenetic tree that are connected through a single third node are called neighbors. For example, in the final tree shown in Figure 8.13 nodes 2 and 4 are neighbors and so are nodes 5 and 6, but not nodes 2 and 3. The neighbor-joining method proceeds by calculating for each possible pair of neighbors the total length of the branches of the corresponding tree. At each stage the pair of neighbors is chosen which induces the shortest tree. This method recovers the correct phylogeny from additive distance data [212] in the same number of steps as the UPGMA method explained above. As with the UPGMA algorithm, we start with an by distance matrix. The neighbor-joining algorithm then consists of four iterated steps: 1. For each pair of taxa

where

compute

is the sum of the distances from taxon to all other taxa :

2. Pick the pair of taxa with the smallest value of and cluster them to form taxon . The distances from to the other members of the data set are

3. Finally, compute the branch lengths from to taxa and :

and

4. Replace taxa and by and reduce generate the last branch with length

by one. If and stop.

go to 1. Otherwise,

To see this algorithm in action, consider again the distance matrix that defeated the UPGMA algorithm in Figure 8.15. Figure 8.16 shows how the neighbor-joining method recovers the correct tree. Application of the neighbor-joining algorithm to the primate data (Fig. 8.10) results in the phylogeny shown in Figure 8.17. When comparing this to the corresponding UPGMA tree in Figure 8.14 the two main differences, unrootedness and absence of a molecular clock, can clearly be seen.

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Fig. 8.16. Neighbor-joining method. The top triangle of the distance matrices contains the raw distances, while the corresponding -values are displayed in the lower triangle; hence in step 1 for example, , where the first element of the sum needs to be transposed from the top triangle of the matrix (see text for further details). Nodes are labeled by bold numbers. Italicized numbers indicate branch lengths.

gibbon

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Fig. 8.17. Primate phylogeny computed from the data shown in Figure 8.10 using the neighbor-joining algorithm. The branch leading to chimpanzee is imperceptibly short.

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8.5 Maximum Parsimony In contrast to the distance methods already discussed, both maximum parsimony as well as maximum likelihood (Section 8.6) are strictly speaking only methods for evaluating a given phylogenetic tree. In practice, they are always combined with a search algorithm looking for the best tree. Under maximum parsimony we seek the tree that implies the fewest mutations along its branches. One of the most popular algorithms for calculating the number of mutations implied by a tree has become known as Fitch-parsimony in honor of its inventor Walter Fitch [77]. The algorithm takes as input a set of character data such as a multiple sequence alignment and a tree (Fig. 8.18). The algorithm implies a model of symmetrical mutation and can there-

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Fig. 8.18. Fitch-parsimony algorithm [77]. The three possible unrooted trees for four taxa get arbitrarily rooted and are then evaluated at each position of the alignment. Each internal node is labeled according to the following convention: if the intersection of the labels on the child nodes is empty, the union of the child labels is taken, otherwise the intersection. Labels generated through the union operation are boxed. The number of boxed labels is summed to generate the “length” of a tree.

fore only be used to derive unrooted trees. However, the tree traversal underlying the algorithm requires a rooted tree as input. This problem is overcome by arbitrarily rooting the given tree. Next, the algorithm goes through every position in the align. The quantity can be ment in order to compute the “length” of the tree, thought of as the number of mutations implied by the tree. Our input data is a multiple sequence alignment from which an initial rooted tree is constructed. At each alignment position, , the following steps are carried out: 1. Label the leaves with the character of the corresponding taxon at

.

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2. Carry out a depth-first traversal of the tree and label each internal node by forming the intersection of the labels of its two child nodes. If this intersection is empty, the parent node is labeled by the union of the child labels, otherwise it is is increased labeled by the intersection. In case of an empty intersection, by one. Once all trees have been evaluated, the shortest tree is chosen as the most parsimonious evolutionary hypothesis. This procedure is illustrated in Figure 8.18. Here the and, hence, under the maximum parsimony criterion it is selected shortest tree is and have the same value of . as the best tree. Notice, however, that trees In fact, the parsimony length of a tree can only vary between the number of variable positions in the alignment, , and half the number of its branches times . The upper limit is due to the fact that at each variable position in the alignment a maximum of one mutation per pair of child nodes rooted on the same parent node can be accumubranches, where is the number of taxa considered, lated. A rooted tree has and, hence, The value of is usually much smaller than the number of possible trees and as a consequence, numerous tree topologies may correspond to a given tree length.

gibbon orangutan gorilla chimpanzee human Fig. 8.19. Maximum parsimony phylogeny computed from the data shown in Figure 8.9. Rooting by designating gibbon as the outgroup.

For our primate example data in Figure 8.9, . The most parsimonious tree computed from this data is shown in Figure 8.19. Notice that this topology contains no information about branch lengths, only about branching order. It is unique and has length 17, that is one step more than the theoretical minimum necessary to account for the data. In other words, the alignment must contain one position that can only have arisen from two mutations. This position is marked by an arrow in Figure 8.9 and implies that, given the tree in Figure 8.19, either the T or the C must have arisen twice in the course of evolution. The reasoning underlying this conclusion is illustrated in Figure 8.20. A character that arises twice in the course of evolution is known as a homoplasy and the number of homoplasies on a maximum parsimony phylogeny . is

8.6 Maximum Likelihood C

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Fig. 8.20. Homoplasy in position 41 of the primate sequence data in Figure 8.9. The two alternative reconstructions of ancestral character states each imply two mutations, shown as bold branches.

It was said that the tree length equals to a first approximation the number of mutations implied by the phylogeny. Now, the number of mutations that might have taken place in the history of a sequence sample varies between zero and infinis bounded ity when in fact we have seen that under the parsimony criterion . Equating with mutations is therefore only reasonable unabove by der a model of evolution where each character analyzed has mutated only once. This is approximated in reality by closely related DNA sequences, such as those shown in Figure 8.9. For highly divergent sequences the parsimony criterion may lead to an increasingly rapid convergence on an incorrect tree as more and more data are analyzed [69]. This problem is overcome by using an explicit model of evolution and searching for the tree which has the greatest likelihood of having generated the observed data under the model. This method is known as “maximum likelihood”.

8.6 Maximum Likelihood Maximum likelihood is a framework for statistical inference. Under this framework the central quantity considered is the probability, , of observing some data, , . This probagiven a hypothesis, , about how this data was generated: bility is called the likelihood of . In maximum likelihood inference we seek the hypothesis with the highest likelihood. Consider for example the DNA sequence TACTTCCTGT. We wish to infer its GC-content, . If we assume that positions in our sequence evolve independently from each other and that the GC-content is uniform across the sequence, we can , by noting calculate the probability of the observed sequence data given , that it contains 4 G/C and 6 A/T residues, and remembering that the probability of observing an A or a T is the complement of the probability of observing a G or a C: (8.1) The graph of this likelihood function is displayed in Figure 8.21. We now wish to compute the maximum of Equation (8.1) by taking its derivative with respect to , setting the result equal to zero and solving for . This is best done after first taking the logarithm of Equation (8.1):

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Fig. 8.21. The likelihood of the DNA sequence TACTTCCTGT as a function of its GC-content, . This is a plot of Equation (8.1).

and differentiating

which yields . This is equal to the fraction of G/C residues in the original sequence. In fact, the fraction of G/C residues is the maximum likelihood estimator of . Maximum likelihood was introduced into statistics by the English population geneticist Fisher and first applied to phylogenetic reconstruction by Edwards and Cavalli-Sforza in 1964 [55]. Given some data, , and an evolutionary model such as the Jukes-Cantor model, , the most likely tree is the tree that maximizes the probability of the data, given the model and the tree: . The search is computationally expensive as it ranges over all possible for states at each internal node in a given tree, over all possible branch lengths in that tree and over all variable positions in the underlying multiple alignment. An efficient algorithm for estimating these branch lengths based on dynamic programming was introduced by Felsenstein in 1981 [70]. Figure 8.22 displays the maximum likelihood phylogeny based on the primate . If we switch the position data of Figure 8.9. The tree has a log-likelihood of of chimpanzee and gorilla in Figure 8.22 the resulting topology has a log-likelihood . In order to test whether the two topologies are significantly different, of

8.7 Searching Through Tree Space

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orangutan gibbon

gorilla chimpanzee human

Fig. 8.22. Maximum likelihood phylogeny computed from the primate sequence data shown in Figure 8.9. Notice that the branches leading to chimpanzee and gorilla are imperceptibly short.

the likelihood ratio test can be applied [70, 125]. This is a general statistical test of the goodness-of-fit between two alternative models. The test is based on the ratio, , of the likelihoods of the two models being compared:

approximately follows a -distribution. For our example, . Since the phylogeny has branches whose lengths were estimated with a one-parameter substitution model (Jukes-Cantor model), we and seven degrees of freedom the crithave seven degrees of freedom. For ical value of the -distribution is 14.07 and we conclude that based on the data in hand we cannot reject the alternative tree where gorilla is the closest relative of man instead of chimpanzee.

8.7 Searching Through Tree Space Optimality criteria such as parsimony or likelihood still leave the question open of how to efficiently search the vast space of possible phylogenies. There is no solution to this problem available that runs in polynomial time and is guaranteed to find the optimal tree. Therefore, a lot of thought has gone into designing heuristic searches in “tree space” as well as shortcuts in the full search. The central problem of heuristic search methods is that they might lead to a local instead of the global optimum. The probability of ending up with a suboptimal search result depends not only on the method of navigating tree space, but also on the strategy of pursuing a better tree once a candidate for optimality has been found. A “greedy” approach would take the first tree with, say, a better likelihood and investigate its neighborhood of trees. However, such a strategy might quickly lead to a local optimum with little opportunity for finding a better tree that is separated from the present tree by one or more intermediate trees with a lower score (Fig. 8.23). In the following sections we start by looking at two heuristic search methods, nearest neighbor interchange and subtree pruning and regrafting, before explaining an efficient optimal method for searching tree space known as “branch and bound”.

8 Phylogeny

Score

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Tree Space Fig. 8.23. Searching in one-dimensional tree space. Each tree has a score attached, for example, a likelihood. The dot indicates the starting tree. A greedy search strategy that always pursues the next tree that is better than the present one would lead to the local optimum ( ) instead of the global optimum ( ).

8.7.1 Nearest Neighbor Interchange An intuitive method for moving in tree space is to consider a quartet of subtrees connected by an internal branch and to look at the two alternative topologies this quartet may have. In an unrooted bifurcating phylogeny there are internal branches, trees can be reached from any given tree using nearest which means that neighbor interchange (Fig. 8.24). This is in most cases a very small proportion of tree space and we may think of nearest neighbor interchange as a method for examining the “microenvironment” of a given tree in tree space. However, any one of the trees

Fig. 8.24. Nearest neighbor interchange. A quartet of subtrees is rearranged into the three alternative possible topologies.

reached by nearest neighbor interchange may become the starting point for a new

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round of nearest neighbor interchange, resulting in a higher portion of the tree space searched. 8.7.2 Subtree Pruning and Regrafting An alternative method for searching tree space is illustrated in Figure 8.25: a subtree taxa we is removed and reinserted at a new position. If in a tree for remove a subtree containing species, we obtain possible points to reinsert the subtree. One of these is the original point and, hence, we get neighbors at each internal branch. For each external branch neighbors can be inferred. Since there are exterior and interior branches, the total number of neighbors that can be reached is . Not all of these are distinct but nevertheless, subtree pruning and regrafting leads to a wider search of tree space than nearest neighbor interchange.

internal

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Fig. 8.25. Pruning and regrafting of internal and external branches on a tree with five taxa. Dotted lines indicate pruned branches and bold lines indicate branches receiving a graft.

8.7.3 Branch and Bound The space of all possible trees describing a given number of taxa can itself be depicted as a tree (Fig. 8.26). The method of branch and bound starts from the smallest possible tree consisting of three taxa. Going outward from the original tree, successive topologies are generated by sequentially adding a new branch to each internal branch of the ancestral tree. Each tree generated along this outward path is evaluated by, say, the parsimony criterion. Once the first set of trees consisting of all taxa have been found, their smallest parsimony score becomes the upper limit for any as yet unfinished topologies awaiting completion. If the best complete topology found so

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far has a parsimony score of , then any subtree in the tree of trees rooted on a tois ignored. The example in Figure 8.26 demonstrates pology that has a score of this principle. The best tree found at the depicted stage of the algorithm has length 7. Hence the trees that can be reached from internal nodes (of the tree of trees) whose score is 7 need not be considered, as their lengths must be 7. This strategy potentially saves a lot of computation time while still guaranteeing that the best tree is found.

ACGTGAC CT..... C.AG... C.A.AC. C.A.A.T

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Fig. 8.26. Branch and bound traversal of tree space. Left: Alignment of DNA sequences taken from five taxa, ; dots indicate identity to nucleotide in top row. Middle: The nine out of a total of 19 topologies that need to be assessed during a search for the most parsimonious unrooted phylogeny of . Right: Number of mutations implied by the corresponding topologies. The optimal phylogeny and its score are boxed.

8.8 Bootstrapping Phylogenies In Section 8.2 we used likelihood-mapping to show that the phylogenetic signal in the globin alignment was quite strong, though not perfect. This indicates that some parts of the tree might not be fully resolved, i.e. some of the quartets fit more than one of the three possible trees. There are a number of methods for locating these unresolved regions and the bootstrap is perhaps the most widely used approach to this problem. Classical statistical parameter estimation is usually based on the following scenario: Given a sample and the estimator of a parameter calculated from this sample, how would this estimator fluctuate if more samples were collected and the estimator was recalculated from each of the new samples? In practice it is often not feasible to obtain more samples. However, if the distribution of the desired statistic is known, a confidence interval for the initial parameter estimation can be computed. Unfortunately, in many cases this so-called null distribution of the estimator in question is unknown. In such a situation the bootstrap can often be applied successfully. This

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consists of resampling from the original sample with replacement and recalculating the desired statistic from each of these bootstrap samples to obtain its distribution. The bootstrap was first proposed by Efron in 1979 [56, 57] and first applied to phylogenies by Felsenstein six years later [71]. With phylogenies the bootstrap works as follows. The input data is a multiple alignment of the kind shown in Figure 8.9. This is essentially an by matrix of amino acids or nucleotides, where is the number of sequences and the length of the alignment. 1. Compute a phylogenetic tree, , from the multiple sequence alignment and save it. 2. Generate a bootstrap sample by randomly drawing times with replacement columns of nucleotides from the original alignment. 3. Compute a tree, , from the bootstrap alignment using the same algorithm as in 1. 4. Record the clusters in that also appear in . 5. Repeat steps 2–4 many times and sum the number of times a cluster in also appears in . An example of bootstrapping a multiple sequence alignment is shown in Figure 8.27. Notice two things: (i) some columns of the original sample appear more than once in the bootstrap sample, others are left out. This is due to “sampling with replacement”. (ii) The order in which the columns of nucleotides appear in the bootstrap sample is quite different from their original order. The order of nucleotide columns has no influence on the topology or the branch lengths of a phylogeny. This corresponds to the standard assumption in phylogenetic reconstruction that residues evolve independently.

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Fig. 8.27. The bootstrap procedure in phylogenetic reconstruction. Each of the bootstrap samples would subsequently be subjected to phylogenetic analysis as further explained in the text.

Figure 8.28 shows the result of applying the bootstrap procedure to our globin alignment. Next to each node there is a number which indicates in how many of

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the bootstrap trees the same set of leaves of the respective subtree was clustered. The higher this number, the more reliable is the grouping of the corresponding sequences. Notice that the (pseudo) root does not have a bootstrap number. This is because its bootstrap support would always and trivially be maximal.

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Fig. 8.28. Bootstrapped phylogenetic tree of seven globin sequences using the neighborjoining algorithm. The numbers next to the nodes indicate how many times the corresponding cluster appeared in 1,000 trees computed from data sets generated from the alignment shown in Figure 5.1 using the bootstrap procedure (cf. Fig. 8.27).

8.9 Summary Phylogenetic reconstruction is concerned with uncovering the evolutionary history of a group of taxa, given data on homologous characters. The result is usually a bifurcating tree. However, it is a priori not clear that the data support a tree. The type of phylogenetic signal contained in the data can be explored using statistical geometry or its more modern version, likelihood-mapping. Phylogenies are either rooted or unrooted. Rooting can be achieved by assigning an outgroup or by assuming a molecular clock. There are three classical approaches to phylogenetic reconstruction: distance methods, maximum parsimony, and maximum likelihood. Distance methods may be based on ultrametric or additive distances. Ultrametric distances conform to a molecular clock and are the assumed input to the UPGMA algorithm, while the neighbor-joining algorithm builds a tree that reflects additive distances. Maximum parsimony and maximum likelihood are methods for evaluating a given tree compared to a set of possible alternative trees. The tree length returned by maximum parsimony can be used to quantify the amount of parallel evolution, or homoplasy, implied by the tree. Maximum likelihood tree reconstruction is based on an explicit

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evolutionary model and allows pairwise comparison of alternative phylogenies by the likelihood ratio test. This gives an overall assessment of the quality of a phylogeny. The bootstrap procedure, in contrast, can be used to assess the robustness of individual subclusters in the tree.

8.10 Further Reading Swofford and colleagues have written a comprehensive review of phylogenetic reconstruction methods [238]. The monograph by Page and Holmes is a good introduction for those new to the field [194], while Felsenstein provides a more comprehensive and advanced treatment [73].

8.11 Exercises and Study Questions 8.1. Consider the following multiple sequence alignment: A B C D

TCGGTAGGCT ACCGTTCCAT ACCCAAGGCT ATGGTAGGCT

How many rooted and unrooted phylogenies can you construct out of the taxa shown in the alignment? 8.2. Compute the pairwise number of mismatches between the taxa in Problem 8.1 and write down the corresponding distance matrix. 8.3. Use the UPGMA algorithm to compute the phylogeny of the taxa in Problem 8.1. 1. Is this phylogeny rooted? 2. Are the distances in the distance matrix ultrametric? 8.4. In Section 8.4.1 it was stated that a necessary condition for ultrametricity is that distinct values, where is the number of taxa analyzed. distances only take Is this condition also sufficient for ultrametricity? Phylogeny (Section 8.5. Use the bioinformer program Evolution A.4.1) to compute pairwise distances from the alignment of primate DNA sequences displayed there. 8.6. Use the bioinformer program Evolution the UPGMA phylogeny from pairwise distance data.

Phylogeny to compute

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8.7. Use maximum parsimony to find the best tree describing the evolution of the data set shown in Problem 8.1 . 8.8. Consider the following phylogeny

and write down all phylogenies that can be reached using 1. nearest neighbor interchange, 2. subtree pruning and regrafting. What fraction of the tree space is covered by each method? 8.9. Use the bioinformer program Evolution the process of bootstrapping a phylogeny.

Phylogeny to visualize

9 Sequence Variation and Molecular Evolution Sequence variation is the currency of genetics; the central aim of all genetics is to correlate specific molecular variation with phenotypic changes. Aravinda Chakravarti [36]

Sequence variation is detected through the comparison of DNA or protein sequences. In most cases, biologists compare sequences which are related by common ancestry, for instance those shown in Figure 9.1. Such sequences are called homologous. common ancestral sequence

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Fig. 9.1. Diverging sequences accumulate substitutions, here represented as filled circles along the branches of a genealogical tree. Substitutions lead to mismatches in the alignment (at positions 2 and 5). Note that there may be more substitutions than mismatches.

Note that this definition does not imply that homologous sequences are necessarily similar. Members of a set of homologous sequences may vary strongly in their similarity to each other, as was already shown in the alignment of seven globin sequences (Fig. 5.1). Homologous sequences are said to be orthologous if they are derived from a speciation event, such as the -globin sequences from human and horse. Homologous sequences are paralogous if they are derived from a duplication event such as the - and -globins in vertebrates. While we may not be able to infer sequence similarity from the mere fact of homology, recently diverged sequences can safely be assumed to be similar. The converse, that similar sequences are homologous and hence have similar functions, is usually also true. There are exceptions to this rule, however. For example, later on in this chapter we are going to describe a gene thought

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to be associated with human language, FOXP2. Surprisingly, the highly conserved protein sequences encoded by this gene are more similar between chimpanzee and mouse than between chimpanzee and human. It is one of the fundamental aims of molecular evolutionary biology to infer the type and time of past evolutionary events from the presently observable variability of molecular sequences. Such events cover a time scale which ranges from days, as in the evolution of strains of E. coli, to hundreds of millions of years, as in the evolution of the phylum of arthropods. In this chapter we will introduce some basic terminology and expand on the evolutionary dimension of sequence comparison.

9.1 The Record of Past Events

number of substitutions per bp

When comparing homologous sequences, aligned positions may be occupied by identical characters (matches). From an evolutionary point of view and the assumption of parsimony, it is reasonable to conclude that matching characters are derived from the same character in a common ancestral sequence. On the other hand, substitutions of one nucleotide by another during evolution become manifest as mismatches in the alignment (Fig. 9.1). Insertions or deletions of nucleotides lead to alignment gaps. At this point it is important to realize that the number of historical substitutions is greater or equal to the number of observable mismatches (Fig. 9.2). The reason

0

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for this is that multiple substitutions at the same or at homologous sites cannot be directly observed from the present record (Fig. 9.1). This effect is more dramatic for distantly related sequences than for very closely related ones. Much effort in evolutionary modeling is spent on correcting for this effect, as discussed in the following sections.

9.2 Mutations and Substitutions There are two fundamentally different ways in which one talks about DNA or protein sequences. First, one may refer to a particular sequence of a particular individual. For instance, a defense lawyer may state that “the DNA sequence of suspect X did not match the probe”. Second, one may refer to a representative of a large class of equivalent sequences, as in the phrase “the sequence of the human genome was published in the year 2001”. The sequence deposited in, say, the NCBI genome database GenBank represents a class of several billion human sequences, many of which do not even exist yet at the current time. Neither is it implied that the human genome in the database was derived from one particular existing individual. A mutation is a heritable change in the nucleotide sequence of a chromosome. It takes place in a specific individual and will subsequently either vanish from or spread throughout the population. If it spreads throughout the population, the result is called a substitution. For instance, proline at position 4 in human -globin has been substituted by alanine in horse at some time in the past (Fig. 5.1). The number of mutations per nucleotide per individual is called the mutation rate and abbreviated . The substitution rate, abbreviated , measures the fraction of those mutations, per unit time, which have spread throughout the entire population being studied. The substitution rate is also called the rate of molecular evolution. The two terms mutation and substitution are sometimes applied synonymously. This confusion is in part due to the following fundamental property of the two processes. Consider a mutation that originates in a particular DNA sequence with mutation rate per generation. In a finite population (or species) of diploid individuals, new mutations arise on average per generation. However, only a fraction of these new mutants survives and is passed on to descendant individuals in subsequent generations. Substitutions are exactly those mutations which will eventually be present in all individuals of a given population (or species). As explained in Chapter 10, under a neutral model of evolution, the probability for a newly arising mutation to be propagated to the entire population, i.e. to become a substitution, is . Therefore, one has the following fundamental relationship between the rate of substitution, , and : (9.1)

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9.3 The Molecular Clock Do substitutions occur at a clock-like pace, in other words, at a constant rate in time? If so, then such a molecular clock could be used to date evolutionary events, for instance the point in time at which two species diverged. The divergence time of two sequences should be proportional to the number of mismatches in a pairwise alignment, if the following three conditions hold: (i) mutations occur at a constant and known rate, (ii) the fraction of mutations which eventually become substitutions remains constant over time, and (iii) a monotonic relationship exists between the number of substitutions and the number of observable alignment mismatches. Zuckerkandl and Pauling [265, 266] were the first to investigate in detail the relationship between the number of substitutions and divergence time. They compared globins and other protein sequences from various mammalian species and noted that the number of substitutions per unit time between two homologous sequences was roughly constant. Based on these results, Zuckerkandl and Pauling suggested that all proteins evolve at an essentially constant rate, known as the molecular clock hypothesis. The existence of a molecular clock on the level of DNA became one of the central concepts of the neutral theory of evolution, developed by Kimura and Ohta in the 1970s and 1980s [144, 140, 141]. The question of how general is the molecular clock for the evolution of DNA and proteins is still intensely debated today [28]. The seven globin sequences shown in Figure 5.1 are all derived from a “proto”globin which existed some 1.5 billion years ago. Table 9.1 shows the number of mismatches in pairwise alignments of human -globin with five other globins and their divergence times. Note that the percent mismatches at the amino acid level may be equal to, larger, or smaller than at the DNA level. Figure 9.3A shows a strikingly Table 9.1. Mismatch percentages between the coding sequence of human homologous globins .

(1) (2) (3) (4) (5)

human chimp horse carp human

-globin and five

DNA AA time 20 5 80 400 450

using the program align [187] with substitution matrix BLOSUM50 pairwise alignment of the cDNA sequences pairwise alignment of amino acid sequences approximate divergence time in million years

linear relationship between the mismatch percentage and the divergence time. For example, when comparing human - and horse -globins, one finds that out amino acids, i.e. , are identical and of the aligned positions are of

9.4 Explicit Models of Molecular Evolution

A

173

B 100

mismatch (%)

protein identity (%)

DNA AA DNA (0.9876) AA (0.9984)

50

40

30

20

80

60

10

0 0

100

200

300

400

500

40

40

50

time (Myrs)

60

70

80

90

100

CDS Identity (%)

Fig. 9.3. A: Comparison of divergence time and percent mismatches from the globin example (Table 9.1) of DNA (circle) and the corresponding proteins (triangles). Only the coding part of the DNA is considered. Regression lines are plotted solid (DNA) or dashed (amino acids). Numbers in parentheses refer to the correlation coefficients. B: Comparison of percent identity in DNA (coding sequences, CDS) and protein alignments in a set of 110 orthologous genes from humans and rodents. The dashed line represents . Its intersection with the regression line indicates an over-representation of synonymous differences in highly similar genes (identity ) and of non-synonymous differences in less similar genes (identity ).

mismatches. Human - and -globins align identically only in out of amino mismatches. This difference is best explained by the fact acids, i.e. there are that - and -globins were derived from an ancient gene duplication which long million years (Myrs) ago) the emergence of the mammalian linpredates (about Myrs ago. On the other hand, -globins and -globins are present eages about in human and apes, but not in horses. They are derived from a duplication which occurred about 20 Myrs ago. Despite their age, the coding sequences of these two genes are completely identical. In contrast, the relatively short divergence time of 5 Myrs between the orthologous human and chimpanzee -globins was sufficient to accumulate some sequence divergence at the DNA level. This observation contradicts the molecular clock hypothesis. Is it therefore disproven altogether? To answer this question, we have to look in more detail at the ingredients of evolutionary models.

9.4 Explicit Models of Molecular Evolution In the preceding section, only the percent of mismatches in pairwise alignments was compared to the divergence time. The molecular clock hypothesis, however, states a linear relationship between the rate of substitution and divergence time. To investigate this issue further, consider a particular position in a DNA sequence. It may be occupied by any of the four nucleotides, adenine (A), cytosine (C), guanine (G), or thymine (T). At the time of replication, a given nucleotide is replaced by nucleotide with probability . The replacement probabilities can be summarized in a state

174


transition matrix of size . Remember that an empirical state transition matrix for amino acids was already shown in the context of the derivation of the amino acid substitution matrices (Table 2.2). The simplest model of DNA sequence evolution is that all possible state transitions are equally likely. The resulting matrix is shown in Equation (9.2). Only one parameter ( ) is needed in this model and therefore it is called the 1-parameter model, or, after its inventors, the Jukes-Cantor model [130]. A

C

G

T

A C G T

(9.2)

Repeating the process of replication with mutation infinitely many times, eventually any nucleotide occurs with equal probability at any given site. The state transition matrix has the following limiting property

(9.3)

where

and

A slightly more involved, but also more realistic way of modeling the mutation process is captured in the so-called two-parameter or Kimura model. It distinguishes between nucleotide-transitions and nucleotide-transversions. A transversion is the replacement of a purine (A or G) by a pyrimidine (C or T). A transition is the replacement of a purine by a purine or of a pyrimidine by a pyrimidine. This distinction reflects biochemical differences between these two classes of nucleotides and the fact that transitions occur at a different rate than transversions. Two parameters are then needed to define the state transition matrix. A A C G T

C

G

T (9.4)

It would be straightforward to set up a model with the maximal number of 12 parameters. However, it would be difficult to determine numerical estimates for all these parameters from experimental data and to work with such a model in practice.

9.5 Estimating Evolutionary Rates

175

9.5 Estimating Evolutionary Rates With the help of an explicit model of sequence evolution, an estimate of the substitution rate, , can be obtained. Consider two sequences as depicted in Figure 9.1. Looking at a particular position and starting from the ancestral sequence (time ), we that in the descendant sequences this position will determine the probability be occupied by identical nucleotides at time . We calculate this probability recursively and consider the possible changes when going from one generation ( ) to the next ( ). Two cases have to be distinguished. In the first case, both nucleotides . The probability for this is . In addition, were identical in generation and . no mutation occurred on any of the two branches between generations . In the Under the Jukes-Cantor model, the probability for the latter event is second case, the site was occupied by two different nucleotides in generation and a mutation occurred, which made them identical in generation . The probabil, ity for this combined event under the Jukes-Cantor model is because there are two ways in which two distinct nucleotides can become identical. can be expressed in terms of Taken together, the desired probability as For small mutation probabilities ( ), one has . Therefore, terms involving the squared mutation probability may be neglected to obtain the approximation (9.5) Abbreviating the difference difference equation

by

, one derives the following

(9.6) When treating time as a continuous variable and re-interpreting per unit time, the corresponding differential equation reads

as a mutation rate

(9.7) deThis is an ordinary linear differential equation. The initial condition . The solution therefore rives from the fact that nucleotides are identical at time is (9.8) The complementary probability of observing two different nucleotides after divergence for generations is (9.9)

176


Equation (9.9) represents the probability of a mismatch at one particular site in a (gap-free) alignment. Assuming that sites evolve independently, this probability is identical to the fraction of mismatches in the alignment. Let this fraction be denoted , where is the absolute count of mismatches and is the number of aligned by positions. By inserting on the lefthand side of Equation (9.9), solving for , , we obtain an estimate of the compound variable and multiplying by (9.10) We now switch our point of view and re-interpret the above two-sequence genealogy as a two-species genealogy. Because of the equality of mutation and substitution rates under neutrality, stated in Equation (9.1), we only have to replace the mutation rate by the substitution rate. In the Jukes-Cantor model, the substitution rate per unit time is . Therefore, we obtain (9.11) There are two points to note about Equation (9.11). First, it relates the observable quantity to the unobservable quantities and . Second, the product is the expected number of substitutions per site on a two-branch genealogy with divergence time . This can be seen as follows. Assuming a molecular clock, substitutions accumulate independently and with a constant rate. Since substitutions are rare events they are well described by a Poisson-distributed random variable with parameter . Given a divergence time , the total branch length in the genealogy is and the expected number of substitutions per site is given by (9.12) It is now easy to combine Equations (9.11) and (9.12) to obtain (9.13) as a function of alignment mismatches, . Figure 9.4 shows a plot of may be different for different sites, Note, however, that the actual value of and that the righthand side of Equation (9.13) is an estimator for the mean of the . This estimate will be closer to its true value if many sites random variable are considered. One can derive an expression for the variance, , of this estimate. It depends on the number of sites compared, i.e. the sequence length : (9.14) increases the variance tends to zero. The variance has a singularity for , i.e. the variance of the estimate of is infinitely large when two random sequences are compared (Fig. 9.4).

If

9.6 Coding Sequences: Synonymous and Non-Synonymous Substitutions

177

0

10

, ,

−2

10

−4

10

−6

10

10

−3

10

−2

10

−1

Fig. 9.4. Logarithmic plot of the expected number of substitutions, of mismatches per nucleotide, (solid black). In random sequences, lines indicate the variance of the estimate of .

0

10

, vs. the number . Dashed

Equation (9.11) contains neither nor separately. In particular, it is not possible to derive separate estimates for or without further information. In order to calibrate the molecular clock, the relationship between sequence divergence and actual divergence time has to be known at least for one sequence pair from an independent source. Paleontological data is often used for this purpose. Given such a calibration, the divergence times for arbitrary sequence pairs may be inferred. However, in practice there are important caveats to be considered. For instance, the rate of evolution may not be constant over time or sequences may not evolve according to a neutral model. One way to deal with the latter is to take into account possible selective constraints in the process of evolution and to distinguish between synonymous and non-synonymous substitution rates when comparing protein coding sequences.

9.6 Coding Sequences: Synonymous and Non-Synonymous Substitutions In protein-coding DNA, substitutions are grouped into synonymous substitutions, which leave the encoded amino acid unchanged, and non-synonymous substitutions, which also change the encoded amino-acid. Consider an alignment of two proteincoding sequences (Fig. 9.5). In order to compute the implied substitution rate, we first determine the total numbers of synonymous ( ) and of non-synonymous ( , amino-acid-changing) sites. Next, we count the synonymous ( ) and nonsynonymous ( ) mismatches. In analogy to Equation (9.13), one can then derive

178


and , the number of synonymous and non-synonymous substitutions per site, respectively. Under the Jukes-Cantor model, these formulas are and

(9.15) (9.16)

In order to apply these equations, one first needs to obtain and . Consider sequence 1 in Figure 9.5 and assume that the grouping of nucleotide triplets reflects denote the possible the reading frame (i.e. frame in this case). For any site let Phe sequence 1 TTT ::: sequence 2 TTT Phe

Thr ACT ::: ACT Thr

Val GTC :: GTT Val

Val GTC : : GCC Ala

Asn AAT : ACG Thr

Fig. 9.5. Alignment of two coding sequences.

number of synonymous changes at this site. can take values , , , or and can be read from the genetic code table (Table C.4). In sequence 1, we start with the first position of the Phe codon, which, like most first and second codon positions, . At the second codon position we get allows no synonymous changes, i.e. the same result ( ), but at the third position . Switching to the and so on. Any site next triplet encoding Thr, we find at its first position is counted as -synonymous and as -non-synonymous. In the example, the first site of sequence 1 is therefore -non-synonymous, the second site -synonymous and -non-synonymous. Generally, the also, and the third site is total number of synonymous sites in sequence 1, , in a (gap-free) alignment of length is

and the total number of non-synonymous sites is

The total number of synonymous ( ) and non-synonymous ( ) sites in the complete alignment is the average of the numbers from sequence 1 and sequence 2

9.6 Coding Sequences: Synonymous and Non-Synonymous Substitutions

179

and

respectively. In the above example, , , and . For the non-synonymous sites one has , , and . Next, the number of synonymous and non-synonymous mismatches between sequence 1 and sequence 2, and , need to be counted. There may be one, two, or three differences in a pair of codons. If there is only one mismatch, it is counted as one synonymous or one non-synonymous difference depending on whether the amino acid is conserved between the sequences or not. For example, the third position in codon in Figure 9.5 carries one synonymous difference, the second position in codon carries one non-synonymous difference. If there are two or three differences in a codon pair, such as in the last codon of the example, one needs to consider the possible pathways which can produce the observed constellation. Depending on the pathway chosen, different numbers of synonymous and non-synonymous changes may be counted. For example, converting codon AAT to ACG requires at least two changes, one from A to C and one from T to G. The two possible pathways are: pathway 1: AAT pathway 2: AAT

1 non-syn. 1 non-syn.

ACT AAG

1 syn. 1 non-syn.

ACG ACG

Evidently, the number of non-synonymous and synonymous changes depends on which codon position is changed first. This leaves the problem of choosing between the different pathways. One possibility is to consider all of them, but to apply a certain weighting scheme. The simplest weighting scheme assigns uniform and are the arithmetic average of synonymous and nonweights. Accordingly, synonymous mismatches over all possible pathways. This strategy was suggested by Pamilo and Bianchi [195] and is implemented for example in the program DIVERGE, which is part of the Wisconsin software package [86]. According to the Pamilo and Bianchi algorithm, one gets for the above example and . Thus, the numbers of substitutions per site and . The ratio is . This raare tio can be used as a test statistic to detect deviations from a neutral model of evolution. In the absence of selection the ratio should be close to one. Values smaller than one are indicative of purifying selection (the turn-over of amino acids is slower than expected under neutrality) while values larger than one are indicative of strong positive or diversifying selection (turn-over of amino acids is faster than expected). Such tests are implemented, for instance, in the software PAML (abacus.gene.ucl.ac.uk/software/paml.html).

180


9.7 Substitutions in Globin Sequences Table 9.2 shows the values of and obtained with the program dists1 [127] for the globin coding sequences. Linear regression yields correlation coefficients of and for the and data, respectively (Fig. 9.6A). Compared to the for the DNA data shown in Figure 9.3A, it appears correlation coefficient of that the synonymous substitutions have a less clock-like behavior. In particular, the -value for human -globin is puzzling. On the other hand, the -values cluster very tightly around the regression line and produce a similar correlation coefficient as the amino acid data ( ) in Figure 9.3A. Table 9.2. Number of substitutions per bp between human (JC)

(K)

(JC)

(K)

-globin and five other globins . (JC)

(K)

human chimp horse carp human only the coding sequences (CDS) of the genes are considered calculations are performed with the program dists1 [127]; JC refers to the Jukes-Cantor model, K to the Kimura model

The generally observed low ratio of to in the comparison of human globin with its orthologues in chimpanzee, horse, and carp suggest that their evolution is subject to purifying selection (Fig. 9.7A). In the comparison of human -globin with its paralogue -globin one finds less evidence for purifying selection. A reasonable biological explanation for this is that duplicated genes may be less subject to functional constraints and may therefore accumulate non-synonymous changes more rapidly. On the other hand, the coding sequences of the recently duplicated human paralogues - and -globin are completely identical, leading to . Differences in the two genes are limited to the non-coding part. For a more rigorous analysis, it is advisable not to lump paralogues and orthologues together. When the data point for the human -globin gene is removed from the regression analysis, the correlation coefficient for the data increases to the value (gray line in Figure 9.6A). Instead of plotting vs. divergence of time, one may be interested in identifying regions under selection within a gene. for Figure 9.7B shows the result of a sliding window analysis of the ratio the human/horse comparison. The window size in this example was set to 120 bp and the step size to 30 bp. The resulting graph oscillates around its mean value of (Table 9.2). There is a minimum of around position 300, indicating high conservation of the encoded amino acids in this region. This part of the protein corresponds to the heme-pocket. However, the oscillations are in part also due to random fluctuations around the mean value as described by the variance of the

9.7 Substitutions in Globin Sequences

A

181

B 4

substitutions per bp

1.2 3

0.8 2

0.4 1

0 0

100

200

300

400

500

0

0

1

2

3

4

time (Myrs)

Fig. 9.6. A: Number of substitutions per bp for the globin data (Table 9.1) inferred from the alignments and using the Jukes-Cantor model as implemented in the program dists1 [127]. Regression lines for the full data set (five comparisons) are in black. The regression line in gray is obtained when the comparison of human -globin and -globin is excluded from the analysis. The correlation coefficients are quoted in parentheses. B: Plot of vs. for a set of 100 rodent-human orthologous genes. The solid line represents the linear regression line, the dashed line the diagonal .

substitution rate in Equation (9.14). To distinguish random fluctuations of the ratio from deviations caused by selection, a rigorous statistical test is needed.

B

m

an

A hu

human

vs. horse

1.0

hu m

an

1.0

0.5

ch i

m

p

ca rp

ho rs

e

0.5

0.0

0

100

200

300

time (Myrs)

400

500

0.0

0

100

200

300

400

position (bp)

Fig. 9.7. A: Plot of the ratio of non-synonymous to synonymous substitutions ( ) for the five globin comparisons (Table 9.1). Horse and carp -globin, which are orthologous to human -globin, have almost identical values. The paralogues human - and -globin differ strongly. B: Sliding window analysis of the human -globin vs. horse -globin comparison. -axis: position along the coding sequence. Data points were taken in a window of 120 bp and step size 30 bp. and values were calculated with the program dists1 [127].

182


9.8 Applications of Computations of relative synonymous and non-synonymous substitution rates are usually carried out in order to detect selection on protein-coding genes. Such investigations may either concentrate on a single gene of interest, or—given the completion of suitable genome sequencing projects—survey entire proteomes. 9.8.1 A Language Gene? Language is an exclusively human trait. This assertion may surprise some readers, as reports of the allegedly fabulous linguistic abilities of intelligent animals such as chimpanzees and dolphins periodically capture the public imagination. However, we can safely state that no animal is capable of the kind of rule-based, open-ended sentence production that three-year old children have mastered to a large extent. In contrast, no human population without language has ever been discovered. In the same highly specialized way in which some animals can fly and others can swim, we can talk [203]. There is both a “nature” as well as a “nurture” aspect to this ability. Any newborn infant discovers the grammatical rules of whichever language he finds himself surrounded by. However, a childhood spent without verbal interaction leads to a lifetime of linguistic disability [203]. The “nature” part of language acquisition must be grounded in genetics, but until the early 1990s there was no suitable model to study this in. This changed with the publication in 1990 of a report on a family with a dominantly inherited severe speech and language disorder [138]. Affected members of this family, known as KE, are deficient in the coordinated movements required for speech; for example, they have difficulty pronouncing somewhat complicated words such as “rhinoceros”. However, there was immediate debate about the precise nature of the disorder. One opinion was that it consisted of a blindness to grammatical features such as case markers, etc. Others held that the disorder was due to problems in language production due to impaired control of facial movements. Finally, it was argued that the disorder was not attributable to a single cause. This uncertainty about the interpretation of the disorder’s phenotype has persisted to the present day [138]. In contrast, the gene underlying the disorder was discovered in 2001. Forkhead box protein 2, or FOXP2, belongs to a large class of transcription factors known as winged-helix or forkhead proteins. It is located on human chromosome 7 (7q31) and its dominant splice form consists of 715 amino acids. Members of the KE family are His . Disruption of characterized by the single amino acid exchange Arg FOXP2 by a translocational breakpoint in an individual unrelated to KE results in the same speech disorder observed in the affected KE family members [138]. There is a great difference between the disruption of a function and its construction. Clearly, changes in FOXP2 can lead to an impairment of linguistic ability, but so can many other mutations. The question is, how central is FOXP2 for the construction of our talkative phenotype? Presumably language has selective value and this premise led to a study of the evolution of FOXP2 in the wake of its identification [63]. Figure 9.8 displays an alignment of FOXP2 proteins from human, chimpanzee,

9.8 Applications of

183

gorilla, orangutan, rhesus, and mouse. The proteins are characterized by two polyglutamine stretches that are variable. Since this variability does not co-segregate with any known phenotype, it is disregarded in the analysis. However, outside these two regions, only four amino acid replacements have occurred since the divergence of mice and monkeys 70 million years ago. Humans differ from the mouse sequence at three positions, and a fourth substitution differentiates the orangutan. This places FOXP2 among the 5% most highly conserved mammalian proteins [63]. Human Chimp Gorilla Orang Rhesus Mouse

A A A V A A

D D D D D E

N T T T T T

S N N N N N

R

Fig. 9.8. Alignment of FOXP2 sequences. The four amino acid substitutions outside of the poly-glutamine regions are marked by columns of residues. The two human-specific substitutions are shown in bold. The forkhead domain contains the arginine (R to a histidine in affected members of the KE family. Adapted from [63].

) that is mutated

If both the synonymous as well as the non-synonymous polymorphisms in FOXP2 are plotted on the phylogeny of the respective animals, a striking pattern emerges (Fig. 9.9): two of the three amino acid substitutions that separate mice and humans are located on the human lineage. The human population is fixed for these two amino acid replacements. Moreover, along the human lineage only nonsynonymous substitutions have taken place. The null hypothesis that there is no ratio could be rejected with high significance ( ). human-specific In other words, positive selection might have driven the fixation of the human FOXP2 variant [63]. Notice that it was the combination of polymorphism analysis and phylogeny reconstruction that lead to this conclusion. Calculation of substitution rates for FOXP2 irrespective of phylogeny would have simply led to the detection of very ratios. low 9.8.2 Selection in the Human Genome Estimating synonymous and non-synonymous substitution rates is also a key method in comparative genome analysis. In a search for positively selected genes in the genomes of humans and chimpanzees, 13,731 annotated genes from humans were ratios, the compared to their chimpanzee orthologues [190]. Based on their genes with the strongest signal of positive selection included proteins involved in sensory perception and immune defense (Table 9.3). Perhaps surprisingly, the list of genes under strong positive selection also included genes involved in tumor suppression and apoptosis. Some of the genes for spermatogenesis also displayed strong positive selection. Given the great interest in the molecular basis of the difference in cognitive abilities between humans and chimpanzees, it was perhaps disappointing

184


years

Human

Chimp

Gorilla Orangutan Rhesus

Mouse

Fig. 9.9. Synonymous ( ) and non-synonymous ( ) substitutions in the FOXP2 gene plotted on the phylogeny of six mammals. Note that the distribution of polymorphisms along the dotted branches connecting the most recent common ancestor of the clade to mouse and the most recent ancestor of monkeys is unknown in the absence of an outgroup. Adapted from [63].

to learn that genes with maximal expression in the brain showed little or no evidence for positive selection.

9.9 Summary In this chapter we studied the relationship between sequence variation and the history of sequences. The concept of evolutionary relatedness of two sequences is connected to the idea of a common ancestral sequence and is visualized by a tree with two branches. The most widely used method to measure evolutionary relatedness is based on an alignment. Starting from this, the number of evolutionary events separating two sequences may be inferred. Here we concentrated on point mutations and showed how the number of substitutions can be computed. As far as the evolution of protein coding sequences is concerned, the number of substitutions must be disassembled

9.11 Exercises

185

Table 9.3. The top 12 genes showing evidence for positive selection in a genome wide comparison of human and chimpanzee orthologous genes. and : non-synonymous and synonymous substitutions between humans and chimps, respectively; and : non-synonymous and synonymous segregating sites in humans, respectively; LR: likelihood ratio from the likelihood ratio test of vs. in the human-chimp alignment. Adapted from a comparative analysis of all genes in human and chimp by Nielsen and colleagues [190].

Gene Name Function

Human-Chimp Divergence Human Polymorphism LR

PRM1 CMRF35H DGAT2L1 FLJ46156 USP26 C15orf2 ABHD1 SCML1

9 13 10 10 11 18 6 15

0 0 1 1 0 2 0 1

0 0 2 4 1 12 4 0

2 0 0 3 0 4 1 0

10.1 9.3 6.6 6.4 6.2 6.1 5.8 5.7

8 8 11 8

0 0 0 0

2 1 2 1

1 0 1 1

5.7 5.5 5.5 5.4

Substitutes for histones in sperm Leukocyte membrane antigen Fatty acid synthesis (presumed) Unknown Testis-specific expression Testis-specific expression Unknown Transcriptional repressor, embryonic development (hypot.) OR2W1 Olfactory receptor LOC389458 Unknown APOBEC3F Antiretroviral factor MS4A12 Unknown

into two components—synonymous and non-synonymous changes. By considering the ratio of synonymous to non-synonymous substitutions, selection can be detected.

9.10 Further Reading Wen-Hsiung Li has written a comprehensive textbook on molecular evolution [168].

9.11 Exercises 9.1. Why is the maximal number of free parameters in a DNA substitution model equal to twelve? 9.2. Verify the approximation in Equation (10.9). 9.3. What is the expected mismatch percentage, if two random DNA sequences are compared? 9.4. Verify the limiting property of matrix 9.5. Derive

in Equation (9.3).

for the two-parameter model.

186


9.6. How many possible pathways exist if a pair of aligned codons differs at all three positions? 9.7. The Jukes-Cantor as well as the Kimura model assume that different sites in a DNA sequence evolve independently. What must be changed if this assumption is dropped?

10 Genes in Populations: Forward in Time The very small range of selective intensity in which a factor may be regarded as effectively neutral suggests that such a condition must in general be extremely transient. Ronald A. Fisher [76, p. 95] The principal evolutionary mechanism in the origin of species must thus be an essentially nonadaptive one. Sewall Wright [263, p. 364]

Genetic diversity is ubiquitous and some of the best-known examples in humans include the different sex chromosomes, the different blood groups, and the presence or absence of genetic diseases such as cystic fibrosis. In fact, except for monozygotic twins, all humans are genetically distinct. At the molecular level this observation corresponds to the knowledge that no two humans have the same genome sequence. Although there is evidence for abundant large scale genetic variation between humans [126], a major fraction of the hitherto studied human variation concerns single nucleotide polymorphisms (SNPs). When comparing two homologous sequences from humans, there is approximately one such SNP per kilobase [244, 11]. However, this number can vary widely between different genomic regions, between different populations and it can be very different in other species. As Gillespie has put it, it is the “great obsession” of population geneticists to account for the causes and consequences of genetic diversity found in natural populations [87, p. 4]. A population is a reproductive community of organisms belonging to the same species. Figure 10.1 illustrates that a genealogical tree of organisms taken from the same species is generally much shallower than a species tree. As a consequence, the number of mismatches and indels found in intra-specific alignments is generally much smaller than in inter-species comparisons. This, in turn, has important implications for the way we interpret and model sequences and their polymorphisms.

10.1 Polymorphism and Genetic Diversity In Chapter 9 we have stressed the fact that new mutations originate as a single copy. The chromosome carrying the lone new variant or allele can be passed on to multiple descendants in subsequent generations. In this way, the frequencies of the novel and of the previously existing allele, also called wild-type allele, may change. Depending on factors such as chance and reproductive success of their carriers, both alleles will

188

10 Genes in Populations: Forward in Time

...ACAA

past

GTAA..

.

deletion(4)

5 Myrs

A(8)−−>G(8)

within species:

genetic diversity

50 kyrs

G(4)−−>C(4)

.

.

G..

G..

.

.

G..

TA

TA

A..

TA

−G

−G

CA

CA

...A

...A

TA

−C

AG

CA

CA

...A

...A

Chimpanzee

present

Human between species:

divergence

Fig. 10.1. A genealogical tree of DNA sequences. The black lines indicate the genealogical history of a sample of four homologous DNA sequences. While the most recent common ancestor of human and chimpanzee existed about 5 million years ago, the last common ancestor of the three human sequences belonged to an individual who lived approximately 50,000 years ago. The gray lines indicate lineages which are not present in the sample or which became extinct. The amount of variation is generally much smaller within species (genetic diversity) than between species (divergence).

be present for some time in the population. However, eventually only one allele will survive and there is some chance that the new allele will have substituted the previous wild-type allele (Fig. 10.2). The fluctuation of allele frequencies due to chance and random fixation of one or the other allele is called random genetic drift. The simultaneous presence in a population of two or more alleles at a defined position in the genome is called a polymorphism. A single nucleotide polymorphism, or SNP, refers to a polymorphism at a single nucleotide site in the genome. Its cause is a point-mutation, i.e. a single base exchange. The vast majority of SNPs are bi-allelic. An example is the G C polymorphism in the human lineage in Figure 10.1, with the two alleles G and C at position . Another type of frequently occurring polymorphism is due to replication slippage and becomes manifest as length polymorphism,

10.1 Polymorphism and Genetic Diversity

189

frequency of mutant allele 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

mutation

339

time (generations)

389

439

polymorphism 489

539

589

mutation

polymorphism

substitution

Fig. 10.2. Polymorphisms originate as mutations and may turn into substitutions due to random genetic drift. A computer simulation was performed under a two-allele neutral WrightFisher model with parameters and . The trajectory shown and originating at generation eventually reaches frequency . In practice, the term “polymorphism” is often used only for those cases in which the frequency of the minor allele is ; this corresponds to the area shaded in gray.

in particular in tandem repetitive DNA. Stretches of tandemly repeated DNA are also called micro- or minisatellites, depending on the size of the repeat unit (Fig. 10.3A). Typical microsatellites have repeat units of length 2-3 bp, minisatellites of up to several 100 bp. Microsatellites usually are multi-allelic and constitute important genetic markers because of their high variability between organisms. The average amount of polymorphisms measured within a population and across some genomic region is called genetic diversity. The first study of genetic diversity in D. melanogaster at the nucleotide sequence level was published by Kreitman in 1983 [152]. He sequenced a stretch of 2,721 bp at the Adh locus in a sample of 11 flies. Kreitman found 43 single nucleotide polymorphisms among the 2,721 sites. In addition, his data set contained six indels, yielding a total of 49 variable sites. Two out of the 11 sequences were identical, of the remaining nine each occurred only once in the sample (Table 10.1). Notice, however, that Drosophila strain Wa-F is distinguished from strain Af-F by a single nucleotide in the length of the insertion located in the 3’ untranslated region. In our subsequent treatment of this data set we will ignore this polymorphism and say that it contains a total of nine distinct alleles. We can also combine the analysis of genetic variation found within a population or species with that found between species. For example, we may compare two se-

4.7

% polymorphic

1.7

690

12

.....AT.. ......... ......... ......... AG...A.TC ......... ......... AG...A.TC AG...A.TC AG...A.TC AGGGGA...

. . . . . . .

Intron 1 CAATATGGG

Average number of nucleotides compared

0

0

87

3

63

Polym. Sites

Nucleotides

... ..C ... ... ... ..C ..C TGC TGC TGC TGC

CCG

5’ Flanking Exon 1

Wa-S Fl-1S Af-S Fr-S Fl-2S Ja-S Fl-F Fr-F Wa-F Af-F Ja-F

Strain

. . . . . . . G G G .

C .. .. .. .. .A .. .. .. .. .. . T.

. . . . . . .

GC

1.0

99

1

. . . . G G G G G G G

T

3.1

65

2

.. .. .. GT GT .. .. .. .. .. ..

AC

1.0

405

4

TT.A TT.A .... .... .... .... .... .... .... .... ..A.

CCCC

7.1

70

5

CA.TA CA.TA ..... ..... ..... ..... ..... ..... ..... ..... ..G..

GGAAT

3.5

264

9

AC....... AC....... ........A ........A ......... ...T.T.CA ..GTCTCC. ..GTCTCC. ..GTCTCC. ..GTCTCC. ..GTCTCC.

CTCCACTAG

C

1.1

178

2

... ... ... . -1 . C 3 . C 4 . C 4 . C 4 G C 4 G C 5 G C 4 .

A

Exon 2 Intron 2 Exon 3 Intron 3 Exon 4, translated 3’ Untranslated

... ... ..T TA. ... ... ... ... ... ... ...

0.6

767

5

.... .... . 1 A. ..... ..... .T... ..... . . . .. ..... . . . .. . . -1 . .

3’ Flanking AGC C T

Table 10.1. Polymorphism at the Adh locus of Drosophila melanogaster. The boxed strains are identical (ignoring the length polymorphism at insertion ). The F and S in the strain designations refer to the fast and slow allozyme variants of the Adh enzyme. : insertion/deletions numbered from left to right; numbers in the insertion/deletion columns refer to length differences compared to the consensus sequence. The underlined polymorphism in exon 4 causes the Thr/Lys amino acid replacement that underlies the fast/slow allozyme polymorphism. Data taken from [152].

190 10 Genes in Populations: Forward in Time

10.2 The Neutral Theory

A

191

B

single nucleotide polymorphism: indiv.1

...ACGTTCGT....

indiv.2

...ACATTCGT....

Chimp1 Chimp2 Human1 Human2 Human3

...TCAGTAA... ...ACACTAA... ...ACAGTAG... ...ACACTAG... ...ACAGTAG...

small−tandem−repeat polymorphism: indiv.1

...ACGGAGAGAGAGAGATTCGT....

indiv.2

...ACGGAGAGAGATTCGT....

fixed shared private

Fig. 10.3. A: Comparisons within a species or a population. The sketch shows the difference between single nucleotide vs. length polymorphisms; the lower example depicts a dinucleotide tandem repeat as found in microsatellites. The two alleles differ in their numbers of dinucleotide repeats. B: Comparison within and between species or populations. According to their distribution, the polymorphisms found in the five individuals are shared, private, or fixed differences.

quences of a particular gene from chimpanzee with three sequences of the same gene from human (Fig. 10.3). A private polymorphism is found only within one of the two species. In our example the private polymorphism only occurs in the chimpanzee. In contrast, a shared polymorphism is variable in both species. A fixed difference, finally, is monomorphic within a species but polymorphic between (Fig. 10.3B).

10.2 The Neutral Theory The contribution of natural selection to shaping the genetic diversity around us has been debated for over a century. Darwin himself wrote [44, p. 103] I am inclined to suspect that we see in these polymorphic genera variations in points of structure which are of no service or disservice to the species. In the 1920s the American population geneticist Sewall Wright and his English colleague Ronald A. Fisher started a controversy over the relative importance of adaptive and non-adaptive evolution that was to last until Fisher’s death in 1962 [204]. Fisher saw little role for non-adaptive evolution, while this was an important component of Wright’s models of evolution. However, by the 1950s Fisher’s view that essentially all heritable differences were adaptive had gained an ascendancy that is rather surprising, given Darwin’s skepticism in this matter. In 1966, two studies on genetic diversity in natural populations were published that challenged the prevailing view. One study focused on genetic diversity in the fruit fly Drosophila melanogaster [118, 167], the other on human genetic diversity [104] and both were based on allozyme data. In order to collect these, total protein is extracted from an organism, run on a non-denaturing gel, and then stained for specific enzyme activity. The result is one or more bands on the gel, whose positions

192


indicate the allele of the gene encoding the enzyme [210]. In Drosophila, 39% of the loci investigated were polymorphic, leading to the estimate that 8–15% of all loci in an individual fruit fly were heterozygous [167]. In humans, the amount of genetic diversity uncovered was also surprisingly high [104]. This lead Kimura [139] and, independently, King and Jukes [145] to propose that the unexpectedly high levels of polymorphism at the molecular level were best explained by assuming that the vast majority of them had no influence on an organism’s fitness. Under this neutral hypothesis of molecular evolution, changes in allele frequencies between generations are due to chance, that is genetic drift, alone and not due to selection. As we will see in more detail below, the main effects of drift are: 1. Undirected change of allele frequencies. 2. Removal of established genetic diversity. The speed with which this happens is inversely proportional to population size. Loss of diversity through drift is therefore mainly relevant in small populations. 3. Removal of new mutations. This is important both in small as well as in large populations because mutations are the only source of genetic diversity and hence constitute the “raw material” of evolution. The birth of new alleles through mutation and their death through drift tend to come to a mutation drift equilibrium. According to the neutral theory, most genetic diversity, which can be seen today, is a reflection of this equilibrium [141]. Two lines of reasoning were used to support the neutral theory. The first was put forward earlier by Haldane [102] who proposed that allele substitution is the result of positive selection acting on favorable alleles. However, each substitution is inevitably linked to a number of genetic deaths of those individuals that do not carry the favorable allele. This effect was called the genetic load or the cost of natural selection. Haldane [102] had estimated that a species could tolerate at most one subgenerations in order to cope with the cost of natural selection. stitution every Kimura examined globins (see Table 9.1) and other protein sequences from various amino acid substitution per vertebrates and calculated an average rate of years per amino acids [139]. Assuming a size of the mammalian genome of nucleotides, he estimated that this rate would correspond to nucleotide replacements per year, or one substitution per years. For the average mammalian substitutions per generation [139]. generation size of four years, this amounts to This figure contrasts strongly with Haldane’s result, unless the assumption that all substitutions are selective is dropped. The second line of reasoning, put forward by King and Jukes in 1969, stressed the fact that the genetic code allowed for synonymous substitutions which were not affecting the encoded protein and were therefore invisible to selection (Table C.4). The discovery only a few years earlier of the genetic code and its degeneracy [191] was an essential prerequisite to this argument. In this chapter we consider neutral models of evolution that move forward in time, while backward in time models are introduced in Chapter 11. We start by explaining how population genetic quantities are simulated forward in time, before approaching evolutionary models of increasing complexity and hence realism.

10.3 Modeling Evolution Forward in Time

193

10.3 Modeling Evolution Forward in Time Evolutionary processes tend to unfold over long periods of time. There are exceptions to this rule, such as the evolution of pathogenic viruses and bacteria, which is quick enough to generate vaccine-resistant or antibiotic-resistant strains within a few years—a very short period on an evolutionary time scale. However, even with microbes it takes dedication to observe evolution directly [61]. A popular substitute for direct observation of complex dynamical processes are computer simulations. Figure 10.4 demonstrates how simple simulations of genetic quantities can be carried out forward in time: the population, usually consisting of a large set of haplotypes or genotypes, is represented in its entirety and reproduces from one generation to the next by resampling with replacement. Genetic quantities of interest, for example the frequency of an allele, are then computed either from the entire population, or from a random sample. Generation 1

Sample 1

Generation 2

Sample 2

Generation 3

Sample 3

Fig. 10.4. Simulating the evolution of a population (size ) on a computer by moving forward in time. As the simulation moves from one generation to the next, samples (size ) are drawn from which the statistic of interest, e.g. the frequency of a particular allele, is calculated.

Consider one generation of a population of size consisting of two alleles, and : . The frequency of allele , , is equal to and . Now simulate the process of evolving from the present . For generation to the next by rolling a die six times returning, say, the 1 we draw the first allele ( ), for the 3 the third ( ), and so on. This leads to in the second generation. The allele the allele configuration frequencies have changed and we repeat the random drawing of alleles to produce the next generation. This time the result is ; in other words, has become fixed and extinct, as in the absence of mutation there is no way of regenerating . Thus, genetic diversity has been lost. A standard measure of genetic diversity is the heterozygosity, , which is the probability that two randomly drawn alleles are different. So for we have

194

and for


the diversity is

In finally, drops to and remains stuck at this value in all subsequent generations. Our simple experiment reconfirms two important points about drift: (i) it changes allele frequencies and (ii) it removes alleles from the population irreversibly.

10.4 The Neutral Wright-Fisher Model The neutral Wright-Fisher model is the simplest population genetic model and makes the following assumptions. A population is represented as a set of genes. When considering diploid organisms, i.e. those with a double set of chromosomes such as genes in a population of organisms. The model further ashumans, there are sumes that population size is finite and remains constant over time. Generations are discrete and non-overlapping; therefore, they can be indexed with integers , and so forth. The genes of generation are drawn randomly from the gene pool, i.e. the genes present in generation (Fig. 10.5). Drawing of genes is carried out with replacement since any particular gene may be passed on to more than one offspring. The biological counterpart to random sampling is the assumption of random mat-

Fig. 10.5. Evolution in a Wright-Fisher population. Arrows indicate the transition from generation to by drawing with replacement genes from generation . Note that the frequency of alleles (represented as filled and empty circles) may change due to genetic drift. In this example, the frequency of black alleles decreases from to .

ing or panmixis in sexually reproducing organisms. Randomly mating individuals have no preference for a specific partner based on phenotypic traits or geographic proximity.

10.4 The Neutral Wright-Fisher Model

195

10.4.1 Fixation and Loss of Alleles To formalize the ideas just presented, assume there are two versions of a given gene, the alleles and . Let them be present in a finite diploid population of size in generation with relative frequencies

and

where is a number between and . The frequency depends on the and . outcome of a binomially distributed random variable with parameters The possible states in generation have therefore the conditional probabilities Prob

(10.1)

The righthand side of Equation (10.1) is independent of . This property is called is Markovian with stationary homogeneity. The associated stochastic process transition probabilities and transition matrix

Furthermore, the probabilities Prob are obtained from the matrix product , for any and . The boundaries and are absorbing states. This property corresponds to our observation made in the die rolling experiment that once an allele is lost, it cannot be restored. The complementary allele is fixed and remains for some time then for all . Two so forever. Formally, if realizations of this process are depicted in Figure 10.6. Claim 10.1 Either one of the two alleles will eventually be fixed, i.e. there is a time , such that (10.2) Prob for all times

.

be the number of -alleles at time and P ROOF. Let tive binomial probability with parameters and , i.e.

For any time and independently of any initial conditions it holds that

the cumula-

196


0.8

0.6

0.4

0.2

50

86

100

150

176

200

generation Fig. 10.6. Simulation of genetic drift over generations in a two-allele system. In the gray trajectory allele is lost at time , in the black trajectory allele is fixed at . The initial frequency of allele in both cases is and population size is .

Prob

The last inequality is valid for any value of Prob

for all

. Thus, the probability (10.3)

The limit of the right side of Equation (10.3) is as grows and remains constant. The probability that does not hit one of the boundaries before time can be made arbitrarily small if is allowed to be sufficiently large. Loss or fixation of an allele by drift is certain, but in large populations either outcome may take very long to reach. For large the expression is close to , thus the righthand side of Equation (10.3) is close to one. In the limit of infinitely large populations, there is no drift and allele frequencies stay constant in time. This is one of the two assertions of the Hardy-Weinberg law.


197

10.4.2 The Hardy-Weinberg Law Consider again two alleles, and . Let their frequencies in an infinitely large, panand . The three possible genotypes, the two mictic population be denoted by , , and homozygotes AA and aa and the heterozygote Aa, have frequencies . The Hardy-Weinberg law states that (i) from the second generation onwards the allele frequencies remain constant forever and that (ii) the genotype frequencies are uniquely determined by the allele frequencies and vice versa by the relationship

In other words, alleles are assembled independently into genotypes. The law is named after George Hardy and Wilhelm Weinberg, who formulated it independently in 1908. In practice, one often finds the Hardy-Weinberg law to be violated. This is mainly due to the fact that natural populations are not infinitely large. Other possible factors that may lead to deviations from the Hardy-Weinberg frequencies are differential action of natural selection upon different genotypes, non-random mating between individuals, or population substructure instead of panmixis. 10.4.3 Fixation Probability and Time to Fixation We return now to a population of finite size . In contrast to Claim 10.1, stating that either of two alleles will eventually be fixed, the following statement concerns the fixation probability of a particular allele. Claim 10.2 The fixation probability of an allele equals its current frequency Formally,

. (10.4)

P ROOF. There are several proofs of this claim. Recurrence theory of finite state Markov chains provides the adequate framework and a detailed treatment of this can be found in a monograph by Ewens [65]. Here, we only note the following. As a consequence of Claim 10.1

Furthermore, the limiting matrix

has the form

.. .

.. .. .. . . .

.. .

198


Given that

for some , the initial distribution in the state space is , where entry is the -th entry in this at its -th entry, one obtains vector. By evaluating the product

In fact, this is true for any time : given the process is in state fixation probability for allele is equal to

at time , the

The reason is, once again, the time-homogeneity of the underlying Markov process. How long does it take for an allele to be fixed? To answer this question, the mean for, say, allele with current frequency , absorption time has to be determined. Note, that the conditional mean absorption time is . For the latter, the addidifferent from the conditional mean fixation time, tional condition that will be fixed (and not lost) is imposed (Fig. 10.7). A rigorous

time in units of

1

0.5

mean time to absorption mean time to fixation 0

0

0.5

1

initial frequency Fig. 10.7. Time to absorption and time to fixation of an allele as a function of its current frequency. Time is measured in units of generations.

treatment of this problem either within the theory of discrete Markov chains or con-


199

tinuous diffusion processes can be found in the book by Ewens [65]. However, there and is a very instructive approximate solution to this problem. Let . From Equation (10.1) one obtains for the expected change in allele frequency

and, similarly, for the second moment

Treating allele frequencies as a continuous variable , we write instead of . , given the process is currently in state . At the Mean absorption time is next point in time, the process is expected to be in some state and the or counter of the time units which the process has spent before reaching is increased by one. Formally,

Assuming that is twice differentiable we approximate the above equation by its Taylor series up to order two and obtain

Thus, one has to solve (10.5) subject to the boundary conditions

. The solution is (10.6)

With the above choice of boundary conditions, Equation (10.6) represents the mean time to absorption, , given the current allele frequency is . In particular, , the mean absorption time is for a newly arising allele with frequency

For the mean conditional fixation time essentially the same arguments (Eq. (10.1)) can be invoked. One only has to note that the transition probabilities that allele will have to be replaced by the conditional transition probabilities not be lost. At every generation, it has to be ensured that allele leaves at least one alleles can be drawn at random and is the offspring. Therefore, only

200


probability that among there are exactly these transition probabilities are

alleles of type

. Therefore,

(10.7) The differential equation, analogous to Equation (10.5), now reads

Its solution, subject to the boundary condition

, is (10.8)

In particular, for a newly arising allele with frequency

, one obtains (10.9)

Finally we mention the average conditional time to loss of an allele; it is (10.10) Given that the allele is lost and has frequency

, it takes only

generations on average until it is lost. An exact formulation of these arguments was provided by Kimura and Ohta [143] based on the theory of diffusion processes. 10.4.4 Loss of Genetic Diversity We now return to the study of genetic diversity under a two-allele model. Allele is sampled with probability , allele with probability . There are two ways in which two different alleles may be sampled: choose allele first and then allele or vice versa. Therefore, genetic diversity in the two-allele model is (10.11) Note that can also be interpreted as the expected heterozygosity (hence the ) of a diploid individual, that is, the probability that a diploid individual carries two different alleles. Let be a binomially distributed random variable with parameand ters

10.5 Adding Mutation to the Model

201

and note that the second moment may be written in terms of the variance and the first moment as The dynamics of

under random drift can then be determined as follows:

Iterating the above procedure, one finds (10.12) Thus, heterozygosity decays under random genetic drift at a rate of per generation. This is another way of stating that drift reduces genetic diversity. As an immediate consequence of Equation (10.12), one finds the “half life” of heterozygosity (10.13) With the approximation

, it follows that (10.14)

The half life of heterozygosity depends linearly on the population size. The above arguments all rest upon the assumption that there are no new mutations entering the population. But in reality, there is continuous influx of new alleles into a population, created by mutation. The key arguments of the neutral theory of molecular evolution rest upon the interplay of drift and mutation, which we consider next.

10.5 Adding Mutation to the Model Depending on the degree of resolution with which molecular evolution is studied, different models of the mutation process are employed. Here, we describe the finite alleles model, the infinite alleles model, and the infinite sites model.

202


10.5.1 Finite Alleles Model The finite alleles model, in particular the two-allele model, has originally been used to describe the evolutionary dynamics in situations where the alleles represent macroscopically observable phenotypes, such as eye or body color. The mutation dynamics in this case is modeled as switching between a given finite number of states. In particular, mutations can also be reversible and there is no influx of new alleles. In fact, one-locus two-allele models with back mutation belong to the classical repertoire of theoretical population genetics and have been treated by all three founders of the subject, Haldane, Wright, and Fisher. In this case there exist nontrivial equilibria of the allele frequencies. In the presence of drift, there is a stationary density of the allele frequency distribution [262]: given a diploid population of and , and mutation rates (for mutation from to ) size , two alleles (for mutation from to ) per chromosome per generation, the stationary and is distribution of the frequency of allele

The mean and variance are

and

If

, then

Abbreviating by , the probability that two chromosomes, drawn at random from the population, are of the same allelic type, is

This expression can also be interpreted as the expected fraction of homozygotes in the population. The expected fraction of heterozygotes is then

With the advent of protein electrophoresis in the 1960s and the observation of unexpectedly high diversity within species [167, 165], the finite alleles model started to be superseded by the infinite alleles model.

10.6 Mutation Drift Balance

203

10.5.2 Infinite Alleles Model With the infinite alleles model [142] it is possible to account for continuous mutational influx into a population. In fact, its name derives from the assumption that any mutation event creates a new allele, which was previously not seen in the population. There is no back mutation to previous states. As a consequence, two alleles can be identical only due to shared ancestry rather than chance mutation to the same allele. Hence identical alleles are said to be identical by descent. In the framework of the infinite alleles model it is possible to decide whether any two alleles are identical or not. However, it is not possible to quantify the difference, for instance in terms of the number of nucleotide mismatches between two different alleles. Put more formally, allele space is a metric space with the trivial metric if if The infinite alleles model is reasonable under many real-world scenarios, when the level of resolution is fine enough. As an example, consider again the data from Kreitman shown in Table 10.1. While he found only two electrophoretic variants in his sample of eleven genes, the number of alleles which are distinguishable at the level of nucleotides was nine. To convince yourself why back mutation can be neglected when dealing with (infinitely) many alleles, consider a sequence of 180 nucleotides. Once this sequence has started mutating, it is unlikely to ever return to its original distinct sequences it can reach. Compare this to the state, as there are ˚ [59, p.35] and you can appreA estimate that our universe has a volume of ciate that the space of possible nucleotide sequences is truly vast even for sequences of only moderate length. 10.5.3 Infinite Sites Model Under the infinite sites model each mutation affects a different position along a stretch of DNA that has never mutated before. This model is realistic when describing and interpreting the evolution of sets of DNA sequences, where the mutation rate per site is low. Allele space in the infinite sites model is equipped with a non-trivial metric. The natural distance between any two sequences is the number of sites at which the two aligned sequences differ. If all sequences have the same length and are alignable without gaps, this metric is also called Hamming distance.

10.6 Mutation Drift Balance 10.6.1 The Rate of Fixation How fast and how often new alleles are fixed is determined by the time to fixation and the rate of fixation. Figure 10.8 shows sample trajectories of newly arising alleles which eventually reach fixation. Under the infinite alleles or infinite sites models

10 Genes in Populations: Forward in Time 1

tf

ix

204

A

relative frequency

0.8 0.6 0.4 0.2

500

700

500

700

900

t

fix

0 300 1

B

relative frequency

0.8 0.6 0.4

tf

ix

900

1/

ft

ix

0 300

µ

0.2

1

C

relative frequency

0.8 0.6 0.4 0.2 0 300

500

700

900

generation

Fig. 10.8. Fixation of new alleles under an infinite alleles or infinite sites model. -axis: time in generations, -axis: relative frequency of mutant alleles. Shown are only the trajectories of those alleles which eventually reach fixation. A: , mutation rate ; B: , mutation rate ; C: , . The time to fixation ( ) depends on (compare A and B), while the rate of fixation depends on (compare B and C). The average time to fixation is generations. At any given time a random number of alleles is present in the population. This number depends on and . Its lower bound estimate is (Eq (10.20)). For our examples (A), (B) and (C). Note that the figures do not display the trajectories that do not reach fixation, which are nevertheless included in this estimate.


205

and in a diploid population, new alleles are generated independently in each generation. Any individual allele is eventually either lost or fixed. There is no equilibrium frequency of individual alleles different from or . The rate of fixation is the product of newly generated mutants per generation times their probability of fixation,

mutation

This is the rate of molecular evolution, or rate of substitution, introduced in Chapter 9. The equality of substitution and mutation rates under the neutral infinite alleles model is an immediate, but nevertheless surprising result given our intuition that the mutation rate is some sort of universal constant, while the rate of substitution should be a function of the population size. Both intuitions are correct and under neutrality the corresponding terms cancel out. This calculation does not hold if mutations are non-neutral.

polymorphic alleles

loss sojourn time:

lost alleles

fixation sojourn time:

fixed alleles =’substitutions’

Fig. 10.9. Mutation drift balance may be visualized as a balance of influx and outflux of (Eq. (10.10)). alleles. The mean sojourn time of a new allele destined to be lost is The mean time to fixation is (Eq. (10.8)).

10.6.2 Number of Alleles We have seen that each individual allele is either lost or goes to fixation. However, as genetic diversity is lost through drift, new diversity is generated through mutation and these two factors lead to an equilibrium between the influx of mutations into the gene pool and their subsequent outflux (Fig. 10.9). We can now ask, is there an equilibrium under mutation and drift for the number of alleles which are simultaneously

206


present in the population at any given point in time? A lower bound estimate of this number was derived by Kimura and Crow [142]. Consider two randomly chosen albe the probability that they are identical by descent, leles in generation . Let i.e. derived from either the same parent or from two alleles which were already iden. The quantity is also called homozygosity. tical by descent in generation The probability that two alleles are identical by descent in generation is derived as . The follows. Either they have descended from the same allele in generation probability of this is in a diploid population of individuals; or they ( ), but have not descended from the same allele in generation have descended from the same allele in some previous generation; by definition this . In both cases neither of the two alleles is allowed to mutate while being is to generation . The probability of this is . passed from generation Summarizing, we can write (10.15) , we simplify the algebra by remembering In order to solve Equation (10.15) for that biologically reasonable mutation rates are small and populations large. Hence, and and write we ignore terms multiplied by (10.16) In an equilibrated population the fraction of alleles which are identical by descent . Thus, when remains constant between generations, i.e. and in Equation (10.16) by , equilibrium homozygosity replacing can be written as (10.17) In case the number of alleles and their frequencies are known, homozygosity is calculated as (10.18) is the frequency of allele . The proportion where is the number of alleles and of homozygotes ( ) in the population is minimal if all alleles are equally frequent,

for all . In this situation, one has (10.19) and a lower bound estimate for the number of alleles in an equilibrated population therefore is


207

(10.20) This number has been called the effective number of alleles by Kimura and Crow [142] and numerical values for this are shown in Figure 10.8. Note that the scaled mutation rate, , which plays a central role in population genetics theory, is featuring again in this formula. For non-uniformly distributed allele frequencies the number of alleles is larger than . In Section 10.7 we will come back to a formula for the number of alleles in a sample drawn from an equilibrated population. 10.6.3 Genetic Diversity There are several measures of genetic diversity. One of them is the number of different alleles in a population. This statistic has the advantage that it is easy to measure. However, its disadvantage is that it is dependent on the size of the sample from which diversity is estimated. This makes it hard to compare measurements which are based on different samples with different sizes. Another measure of genetic diversity is the proportion of heterozygotes in a population or, equivalently, the probability with which two randomly chosen alleles are different. In case of known alleles and allele frequencies, heterozygosity is

On the other hand, and as an immediate consequence of Equation (10.17), equilibrium heterozygosity is (10.21) This result entails one of the corner stones of the neutral theory of evolution [139]. Diversity in neutrally evolving populations is monotonically increasing with the , then . Equation (10.21) applies scaled mutation rate . In fact, if to average heterozygosity. Under the action of mutation and drift, heterozygosity is a Markov process with stationary transition probabilities. For each point in time, het. Three realizations of this stochastic process erozygosity is a random variable, under the infinite alleles model are shown in Figure 10.10. It demonstrates that heterozygosity as a random variable in time fluctuates around the value calculated in Equation (10.21). The entire distribution of at a single locus and under the infinite alleles model has been derived by Fuerst and colleagues [82]. In particular, for the , they obtained variance, (10.22) Equation (10.21) plays a central role and will be rederived with different arguments in Chapter 11. We consider further its implications. Notice first that Equation (10.21) establishes a relationship between heterozygosity , which can be

208


0.8

0.6

0.4

0.2

0

0

100

200

300

400

500

generation Fig. 10.10. Simulation of heterozygosity at three independent loci evolving under the infinite alleles model. Each trajectory represents the heterozygosity at one locus. Input parameters are and , which implies a mean heterozygosity of (dotted line). Notice the fluctuations around the expected heterozygosity. For the parameters considered the standard deviation is (Eq. (10.22)).

measured, and the unknown quantity . As an application consider the data set shown in Table 10.1. If we ignore indels, we observe eight unique alleles and one allele which occurs three times (Fr-F, Wa-F, Af-F). Hence, the observed heterozy, where the factor gosity is corrects for the fact that we are using sample frequencies rather than population frequencies [121]. Based on Equation (10.21) one would estimate , where the hat indicates that the value is the scaled mutation rate to be an estimate. Second, there are estimates of the (unscaled) mutation rate available [51], which means that Equation (10.21) can be used to calculate the population size. For Drosophila, Drake and his colleagues reported a mutation rate of per bp per generation. Thus, the mutation rate for the 2,721 bp investigated in the Adh region would be per generation. This yields an estimate of the po. We need to stress that population size refers here, as pulation size always in our book, to the effective population size, not the census population size. Roughly, effective population size is the number of reproducing individuals. This

10.7 Sampling Alleles from Populations

209

number is generally much smaller than the actual number of individuals. Finally, Equation (10.21) predicts that heterozygosity approaches unity with increasing population size. This prediction was central to attempts in the 1970s to falsify the neutral theory by measuring in the largest populations known, those of bacteria. The first of these studies came to the conclusion that for Escherichia coli , which is far from unity [182]. However, the estimation of from is fraught with difficulties. One of these follows from the shape of the graph of as a function of shown in Figure 10.11. For large values of , cannot be predicted very accurately as the curve flattens out. Moreover, Equation (10.21) is based on the assumption of free recombination, which implies that all loci evolve independently of each other. This assumption is not valid for asexual organisms such as E. coli [222, 175]. A

B

1 0.20

0.01

0.15

0.10 0.0001 0.05

0.0001

0.01

1

0.0

0.2

0.4

0.6

0.8

1.0

Fig. 10.11. A: Expected heterozygosity and standard deviation as function of the scaled mutation rate under the neutral infinite alleles model (Eqs. (10.21) and (10.22)). B: Standard deviation as function of expected heterozygosity. The dashed line is a plot of and shows that for small values of .

10.7 Sampling Alleles from Populations 10.7.1 Ewens’ Sampling Formula A cornerstone of the study of the infinite alleles model is Ewens’ sampling formula [64]. Consider a population in equilibrium under the neutral infinite alleles model and a sample of size drawn from the population. The sample configuration is a vector , where the components denote the number of . Ewens’ alleles represented exactly times in the sample. Then, sampling formula gives the probability distribution for the sample configuration. The , where probability for a particular configuration are non-negative integers with the property , is (10.23)

210


where in a sample of . Then,

. Let further be the number of distinct alleles . The distribution of is

where is the coefficient of in the expansion of . Ewens’ sampling formula is the basis for one possible test of the null hypothesis of neutral evolution. More tests of this hypothesis are discussed in Chapter 12. Here, we apply Equation (10.23) to the experimental data shown in Table 10.1. In this data ; therefore, we are dealing with a vector of the form set sample size is . The probability for this configuration is

For sample size , there are altogether possible configurations. The number of possible configurations is equal to the number of decompositions of into integer summands without regard of order [1, sec. 24.2.1]. The probabilities for each configuration depend only on . Figure 10.12 shows the probability distribution for different numerical values of . The observed configuration is indicated by a vertical black line. Crucial for deciding whether the observation is compatible with the model of neutral evolution is an accurate estimate of . One can show that all information A

B

0

0

10

10

-2

10

-4

10

-6

10

-2

10

-8

probability

probability

10

-10

10

-12

10

-14

10

-16

-4

10

observation

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Fig. 10.12. A: Probability distribution of the possible configurations for a sample of size and different values of . Configurations are ordered lexicographically. For instance, (i.e. all alleles are identiconfiguration corresponds to the vector cal), configuration to , and configuration to the observed configuration (i.e. there are eight singletons and one cluster containing three alleles). B: Probability distribution for and different values for . The observed number of distinct alleles ( ) is indicated by the black vertical line. Vertical lines represent the mean values for the three choices of .

10.7 Sampling Alleles from Populations

211

about which is deducible from a configuration is already contained in the num, such that explicit knowledge of the configuration is not ber of distinct alleles is a sufficient statistic for . Indeed, the conditional needed. In statistical terms, are independent of . It holds that probabilities (10.24) as a product of Thus, when writing the probability and , only the last factor depends on . This implies that a maximum-likelihood estimate of can be based on the probability distribution of . Given an observation , it can be shown that this maximum likelihood estimate is the solution of (10.25) Furthermore, using the fact that under neutral evolution is independent of , one can construct a statistical test for the null hypothesis of neutral evolution. Unlikely configurations of given would lead to a rejection of the null hypothesis. This idea is formalized in the Ewens-Watterson test. Apart from the computation of the probability of an observed allele configuration under the infinite alleles model, Equation (10.23) allows one to deduce the expected number of singletons and the expected number of different alleles. The expected number of singletons in a sample of genes is (10.26) The expected number of different alleles in a sample of

is (10.27)

where the last equality requires some arithmetical manipulations. In contrast, the formula given by Kimura and Crow (Eq. (10.20)) is a lower bound estimate for the is larger number of simultaneously existing alleles in the population. In fact, than only if is sufficiently large. Furthermore, for large values of the expected number of alleles in a sample, , may be smaller than the lower bound estimate for the number of alleles in the population, (see Table 10.2). Finally, we note the variance of . It is

212


10.7.2 Application and are shown For the data presented in Table 10.1, some values of in Table 10.2. The observed number of alleles is . There are eight singletons and one set of three identical alleles. There are two possible configurations with . One of them—the one observed—is , the . From Equation (10.24) other possible one is the conditional probabilities are

The maximum likelihood estimator for

derived from Equation (10.25) is

The observed allele configuration appears to agree well with the expected results for and for the parameter (Fig. 10.12). On the other hand, 43 out of a total of 2,721 sites were polymorphic (or “segregating”), which is somewhat less than the 58.6 segregating sites expected under neutrality (Table 10.1). Is a reasonable estimate for D. melanogaster and a stretch of kb of its genome or do the data have to be explained by other evolutionary mechanisms than mutation and drift alone? After all, the two estimates of , obtained from Equation (10.21), which , and from Equation (10.27), which was , are similar but was not identical. Table 10.2. Numerical values for the expected number of alleles, number of segregating sites and singletons of the data presented in Table 10.1.

0.02 2.00 6.12 20.0

1.020 3.000 7.120 21.00

1.058 4.206 6.629 8.945

0.057 1.947 2.234 1.550

0.586 5.858 17.925 58.579

0.022 1.833 4.176 7.333

lower bound estimate according to Equation (10.20). expected number of segregating sites according to Equation (11.8).

We will come back to the problem of testing the significance of the difference between the neutral expectation and observation in Chapters 11 and 12.

10.8 Selection The neutral theory of molecular evolution should not detract us for too long from the really interesting question, namely how is adaptation of a species to certain environmental conditions possible? Most answers contain in one way or the other the

10.8 Selection

213

concept of an “advantageous mutation” [76]. An organism carrying an advantageous mutation, i.e. a genetic constitution which makes it better adapted to the given environmental conditions, will on average leave more offspring to the next generation than its competitors. Such an organism is said to have an increased fitness. In a very large population, which is the case envisaged by Fisher, the dynamics of such an advantageous mutation can essentially be treated in a deterministic framework. The following model captures the salient features of the spread of an advantageous mutation within a population. Let and be the relative frequencies of the two alleles “advantageous mutation” (allele ) and “wild-type” (allele ), respectively. As always in a two-allele model, . For diploid organisms, i.e. organisms which have two copies of each and be chromosome, let the fitnesses of the three genotypes ,

In other words, organisms with two copies of the advantageous mutation have a fitness difference of compared to those which have two wild-type chromosomes. The dynamics of the -type chromosomes is described by a difference equation which accounts for the change in frequency of type when passing from one generation to the next (10.28) Note, that the denominator on the righthand side represents the average fitness of the population, where the average is taken over the three possible genotypes. In fact, this term is called mean fitness. The difference equation (10.28) is approximated by an ordinary differential equation, the so-called deterministic selection equation (10.29) with the initial condition The solution of this differential equation is (10.30) Since the advantageous mutation is initially present only on a single chromosome, , where is the (effective) population size. The one typically assumes time which the advantageous mutation requires to increase from frequency to is obtained by solving

for . This is

214


(10.31) It can be shown that the righthand side of Equation (10.31) is a good approxima, for an advantageous allele also in a non-deterministic tion of the fixation time, framework. For the above fitness regime, mean fitness simplifies to

Since is a monotonically increasing function in , mean fitness also has this property. In fact, Fisher’s fundamental theorem of natural selection states that “mean fitness of a population increases”. In other words, evolution is a hill-climbing process towards a fitness peak. However, it has to be emphasized that this is true only for the deterministic dynamics of infinitely large populations. In finite sized populations, positive selection and genetic drift act antagonistically. While a positively selected, newly arisen, allele is still rare in a population, there is a non-zero probability that this allele will be lost again by drift. It can be shown [65] that the fixation probability of an advantageous allele is goverend by its selective advantage and that it is approximately (10.32) per generation (see A positively selected allele initially increases at a rate per generation Eq. (10.30)), while diversity is lost due to drift at a rate of (see Eq. (10.12)). Crucial for positive selection to prevail as an evolutionary force over drift is the relation (10.33) Thus, rather than the net fitness advantage alone, it is its relation to population size that is decisive for the possibility of adaptation. In sufficiently large populations, there is a chance even for weakly selected alleles to become fixed. The issue whether the “normal” mode of genome evolution is evolution under weak positive selection is the matter of an old debate [153, 115]. One of the problems, implicated by Equation (10.33), is that the action of weak selection is hard to prove in laboratory experiments. The quest remains to distinguish the action of selection from “molecular noise”. This difficulty has been called the “uncertainty principle of molecular evolution” [235, 242]. The combined action of mutation, selection, and drift introduces rich dynamics into the evolutionary system which allows for a variety of non-trivial equilibria depending on the relative magnitudes of the selection coefficient, dominance effects, mutation rate, and population size [32, 14]. A good part of the mathematical foundation for these models has been laid out by Haldane, Fisher, and Wright in the first half of the past century [100, 101, 76, 262].

10.9 Summary This chapter is concerned with the dynamics of genes in populations. The neutral Wright-Fisher model provides a convenient framework for describing such dynam-


215

ics. This model is based on a population of constant size that evolves from one generation to the next by sampling the genes with replacement. Neutrality then simply corresponds to the fact that all genes are equally likely to get transferred to the next generation. Changes in allele frequencies are random and said to be due entirely to genetic drift. Given such a model without mutation, any existing genetic diversity will eventually be lost from the population. In other words, there will be only one allele for each gene. The probability that a certain allele replaces all others, i.e. is fixed, is equal to the initial frequency of the allele. If we add mutation to the model, the mutation process can be described in three different ways: (i) under the finite alleles model each mutation leads to a random switch between alleles; (ii) under the infinite alleles model each mutation generates a new allele; (iii) under the infinite sites model each mutation affects a different site along a stretch of sequence. Mutations create new alleles, while drift removes alleles from the gene pool and these two factors can come into a mutation drift balance. If mutation is balanced by drift, it is possible to compute the number of alleles expected to occur in a population as well as the probability of drawing a pair of distinct alleles, also known as the genetic diversity. Since the number of alleles can easily be observed, a comparison between observed and expected allele configurations is used as a test of neutral evolution.

10.10 Further Reading This chapter is mainly based on the comprehensive textbook by Hartl and Clark [106] as well as on the concise primer by Gillespie [87]. Either one of these books should be consulted for further details and references on the foundations of population genetics. Ewens [65] gives a mathematically advanced treatment of Markov chain theory and diffusion models in population genetics. Finally, Provine describes the history of population genetics by way of an illuminating biography of Sewall Wright [204].

10.11 Exercises and Software Demonstration 10.1. The bioinformer software contains under Evolution Drift a program that shows forward in time simulations. Work through the tutorial for that program in Section A.4.2. 10.2. In the earliest attempt to test the validity of the neutral hypothesis using bacwas measured in E. coli terial populations, a virtual heterozygosity of [182]. Why was the genetic diversity in E. coli referred to as “virtual” heterozygosity? was assumed. Which popula10.3. In the E. coli study a mutation rate of tion size follows from this under the neutral infinite alleles model?

216


10.4. Based on considerations independent of the genetic diversity, the population . Given this estimate, what is the genetic size of E. coli was estimated to be diversity, , expected under the neutral infinite alleles model? 10.5. The population size relevant for population genetics is usually referred to as . It may differ widely from a population’s head count. effective population size, is the size an idealized population would have, given the effect of Essentially, drift on its allele distribution [106, p. 289]. A population study of E. coli has come , which is surprisingly small [107]. What is the to the conclusion that value of expected from this estimate of population size? 10.6. Show that allele frequencies do not change between generations when , i.e. show that the Hardy-Weinberg law holds if the population size is infinitely large. Hint: Start from Equation (10.1). . What is the probability that two 10.7. Consider a haploid population with randomly selected alleles had their last common ancestor exactly 1,000 generations ago? was found based on the assumption 10.8. In Section 10.7.1 a value of that there are nine alleles in the sample of eleven Adh sequences of D. melanogaster. is included in the analysis. Estimate Remember that there are ten alleles, if indel on the basis of ten alleles. 10.9. Consider a diploid population with alleles. Show that the proportion of homozygotes is minimal if all alleles are equally frequent.

11 Genes in Populations: Backward in Time The circle of ideas that has come to be known as the coalescent has proved to be a useful tool in a range of genetical problems, both in modeling biological phenomena and in making statistical sense of the rich data now available. John F. C. Kingman [149, p. 1461]

Intuitively, we imagine evolution as moving forward in time. Starting perhaps four billion years ago in a marine RNA world, life evolved via the ancestor of all cells, the invention of multicellularity, the conquest of land and air to the present, constantly changing, overwhelming diversity. If we restrict our attention to the microevolution of single populations, we have seen in Chapter 10 that the Wright-Fisher model, which in its original formulation moves forward in time, is useful for capturing the dynamics of genes. In this chapter we turn the Wright-Fisher model on its head to look backward in time. This inversion of the arrow of time forms the basis of almost all contemporary investigations of the dynamics of genes in populations. Such investigations encompass a wide range of problems, in particular understanding the distribution of polymorphisms along and between human genomes [244].

11.1 Individuals’ Genealogies vs. Gene Genealogies We all have parents, grandparents, great-grandparents, and so on. In other words, the number of our ancestors increases exponentially as we move backward in time. This means that your ancestors rapidly also become the ones of any other person you meet. If we assume panmixis and constant, sufficiently large population size, the first common ancestor of all members of a sexually reproducing generations, where is the population population is expected to appear after size [208]. For humans this would place the universal ancestor in the Middle Ages. Perhaps even more surprising than this extremely rapid appearance of universal angenerations the ancestral population cestors is the discovery that after has only two kinds of members: approximately 80% are universal ancestors, and the remaining 20% have left no descendants in the present [208]. Figure 11.1A shows the simulated genealogy of a single member of a sexual diploid population over 20 generations. Going back just five generations we reach the first two individuals that are the ancestors of the entire population. The fact that two such most recent universal ancestors turn up in the same generation is due to

218

11 Genes in Populations: Backward in Time

chance, it might have been just a single individual. Going back further in time we enter a zone, where individuals might be ancestors of all, none, or part of the present all individuals are population. This only lasts for three generations, however, in either ancestors of the entire present day population, or have left no descendants at all. This situation now remains stable ad infinitum. The only fluctuations we observe from now on are in the distribution of the numbers of universal ancestors and “universal losers”. Now, real populations are not panmictic and do not have constant size. Nevertheless, a realistic simulation of human history led to the conclusion that universal ancestors of all of humanity lived around 1,400 B.C. in Asia. Further, from the year 5,353 onward into the past we all have identical ancestors [208]. At this point we come to the important distinction between the genealogy of individuals just discussed and that of genes. In contrast to individuals, each gene has only a single ancestor. This places common gene ancestry much further back in time than common individual ancestry. As a consequence, it is not certain that a single gene of a random individual living today can be traced back to our Asian ancestor of not so long ago. Figure 11.1B traces the ancestry of the two homologous genes contained in a single individual. We have to wait for 18 generations until they find their first common ancestor. In order to learn more about the dynamics of genes in time we now return to the Wright-Fisher model of gene evolution already encountered in Section 10.4.

11.2 Forward vs. Backward in Time The Wright-Fisher model (Section 10.4) is forward in time in the sense that a quantity . Let us look again in generation is a function of the same quantity in generation in detail at the structure of the Wright-Fisher model. The first thing to realize is that for many evolutionary scenarios there is no need to explicitly model diploidy or sexual dimorphism as was done in Figures 11.1A and 11.1B. Figure 10.5 illustrated how through resampling with replacement the correspondingly uniform gene pool is transmitted to the gene pool in generation . The example in generation diploid organisms and hence we simply model population depicted consists of homologous genes. Figure 11.2A shows all lines of descent for our example population throughout a simulation run of 15 generations. Notice that due to sampling with replacement five genes get lost in the transition from generation to generation , while three genes each leave one descendant, three genes leave two descendants and one leaves three descendants. Otherwise, Figure 11.2A is too tangled for easy reading. Figure 11.2B shows its untangled version, which allows tracing of individual lineages. As we pick , and follow its fate through the generations forany gene, e.g. from generation ward in time, it is never clear whether it will go extinct or show up in the present population. Conversely, if we concentrate on the genes in the present generation, , and follow their fate backward in time, we will, given enough time, eventually hit the first gene that was the ancestor of the entire population. In Figure 11.2 the

B

Present

Past

Fig. 11.1. A: The number of ancestors of any sexual organism increases exponentially along its genealogy. : Gene; box: male; ellipse: female; double line: universal ancestor; dotted line: no descendants in present generation; : -th generation; : going back generations. To limit clutter, lineages are only drawn for eight generations. B: The same as A but with superimposed gene ancestry shown in black lines.

A

11.2 Forward vs. Backward in Time 219

2

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Fig. 11.2. Lines of descent of 12 genes for 15 generations under the Wright-Fisher model of evolution, where generation is produced from generation by sampling with replacement. indicates the most recent common ancestor; black lines are the lineages of extant genes; gray lines show extinct lineages.

1

Tangled

220 11 Genes in Populations: Backward in Time

11.2 Forward vs. Backward in Time

221

most recent common ancestor of the population is marked by . Clearly, this most recent common ancestor also has ancestors; in fact, as we go back in time, we find in each generation beyond the most recent common ancestor exactly one gene from which the members of our entire example population have descended. Among these, the most recent common ancestor is very special. The reason for this is that genetic events, such as mutations, that might differentiate the members of our population must have occurred since their descent from this last common ancestor. Conversely, any event along a lineage leading from somewhere in the past to the most recent common ancestor has equally affected all members of the population and is therefore invisible. However, we usually analyze samples of genes taken from large populations. The genealogy of any random sample is a subgenealogy of the population genealogy. An example for the sample of genes 1, 2, 4, and 8 is shown in Figure 11.3. Past

Present

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Fig. 11.3. Lines of descent for a sample of genes form a subgraph of the population genealogy shown in Figure 11.2. indicates the most recent common ancestor of the sample. It coincides with the most recent common ancestor of the population with probability [192].

222


Past

MRCA

CA

CA

Present

4

8

2

1

Fig. 11.4. Coalescent of four genes sampled from the population shown in Figures 11.2 and 11.3. CA: coalescence event; : most recent common ancestor (MRCA); : time interval in which the coalescent consists of lineages.

11.3 The Coalescent Figure 11.4 displays the genealogy of the sample of genes 1, 2, 4, and 8 extracted from their population context (Fig. 11.2). It is a binary tree rooted on the most recent common ancestor. When moving from the tips of the tree, representing the sampled genes, to its root, the most recent common ancestor, we pass internal nodes where two lineages coalesce into their most recent common ancestor. Such a fusion of two lineages is also known as a coalescence event. The complete topology of coalescence events is called the coalescent and the theory built around this data structure is referred to as coalescent theory. It was first proposed in the early 1980s [147, 148, 119, 239, 149] and has in the meantime developed into a subdiscipline in theoretical population genetics in its own right [120, 192]. nodes, Since a coalescent tree is a rooted bifurcating tree, it consists of where is the size of the sample. In other words, construction of such a tree takes time proportional to the sample size. This leads to a great efficiency gain compared to forward simulation, where entire populations need to be represented in computer memory and a possibly very large number of lineages is traced. The vast majority of these lineages do not contribute genes to the sample from which the statistic of interest is calculated (Fig. 11.2). Moreover, the constellation of genes in genera. This leads to autotion clearly depends on their constellation in generation correlated measurements, such as those shown in Figure 10.6. However, statistical analyses are usually based on the assumption of independence, which is best implemented by constructing a separate coalescent for each measurement. Apart from generating independent samples, coalescent simulations have the huge advantage of just tracing the history of a gene sample rather than that of the entire population. A collection of coalescent trees is shown in Figure 11.5. Notice that the time to the most recent common ancestor, i.e. the height of these genealogies, appears to be only weakly correlated with sample size. We will quantify this relationship in

11.3 The Coalescent

223

Section 11.5; but before we do so, we shortly comment on the differences between coalescent and phylogenetic trees.

:

:

:

Fig. 11.5. Examples of neutral gene genealogies in a haploid population for sample sizes 2, 10, and 50. Time is measured in generations, where is the (usually unknown) size of the population.

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11.4 Coalescent vs. Phylogenetic Trees Coalescent and phylogenetic trees are both bifurcating trees, but their construction and application is very different. Phylogenies are usually reconstructions of the one “true” evolutionary history of a set of species. In contrast, coalescent trees are random histories of genes sampled from a single population. Their construction is based on model parameters, which may or may not have been obtained from empirical data. Table 11.1 summarizes the main differences between these two important evolutionary trees. Table 11.1. Comparison between the coalescent and the phylogeny of a sample of genes. Feature Level of comparison Purpose

Phylogeny Inter-species gene history Reconstruct the true species history Observation of interest Tree topology Data source Multiplicity

Comparative data Single

Coalescent Intra-species gene history Simulate sets of potential gene histories Frequency spectrum and distribution of segregating sites Model parameters Many

11.5 The Infinite Sites Model and the Number of SNPs Our first application of coalescent theory concerns the number of single nucleotide polymorphisms (SNPs) expected under neutrality in a sample of genes. Among geneticists SNPs are also known as segregating sites. The number of segregating sites, which can easily be observed from alignments, is a simple function of the , the product of population size, , and mutation rate, . unobservable quantity Our derivation of the number of segregating sites is based on the infinite sites model, according to which no two mutations hit the same nucleotide (Fig. 11.6). This is a reasonable assumption, given that the majority of observed SNPs are bi-allelic (cf. Table 10.1). Under the infinite sites model the number of SNPs in a sample is equal to the number of mutation events in the corresponding coalescent. Hence, the expected number of SNPs is equal to the rate of mutation times the total length of the coalescent. We are now going to derive basic properties of the total length of a coalescent, i.e. the sum of all branch lengths.

11.6 Mathematical Properties of the Neutral Coalescent

225

genealogical history of a sample of two genes

MRCA

present day sequence comparison

Seq 1

Seq 2

Fig. 11.6. Top: Genealogy for sample size . Mutations are depicted as dots along the branches of the genealogy. Bottom: Comparison of the two sequences connected by the genealogy shown on top. Matching positions are shown as vertical lines. Under the infinite sites model the number of (unobservable) mutations is equal to the number of observable segregating sites (SNPs) in the sample. For a given coalescence time, say, the number of segregating sites is a Poisson-distributed random variable with parameter , where is the mutation rate per site per generation. MRCA: most recent common ancestor.

11.6 Mathematical Properties of the Neutral Coalescent 11.6.1 Tree Depth, Tree Size and the Number of Segregating Sites Consider again the coalescent shown in Figure 11.4, which has time intervals marked. Each coalescence event reduces the number of lineages by one. We call the time interval in which the coalescent consists of lineages . Our first aim is to compute the average length of . For this we need the probability at any point in time that no coalescence took place in the previous generation. Consider a gene pool ; that is, from each of members of a diploid population we include its of size two copies of a particular gene, -globin say, in this pool. We move backward in time . As we do so, we invert the traditional timefrom generation to generation leaving descendants in generation forward metaphor of genes in generation

226


and imagine instead that genes in generation pick their ancestors in generation . . There are Two genes pick a particular common ancestor with probability possible common ancestors, hence the probability of two lineages coalescing is in each generation and the probability of them not coalescing is . We now turn our attention to the sample of genes. Obviously, the first gene has some ancestor in the previous generation. The probability that the second gene has a different ancestor is simply the probability of not coalescing,

The probability that three genes each have different ancestors in the preceding generation is

In general, the probability that

genes have no common ancestor is

The approximation is obtained by assuming that population size is large and that or less may be neglected. The probability, , of a therefore terms of order coalescence occurring is the complement of it not occurring, i.e.

Thus, going backward in time, for each generation there is a probability for a coalescence to occur. The probability that the first coalescence occurs after exactly generations is therefore . This means that coalescence times are geometrically distributed with parameter . Hence, the expected time it takes for lineages to coalesce to lineages is . Implicit is the assumption that population size, , remains constant over time. Furthermore, if is large compared to the sample size , then and the geometric distribution is well approximated by an exponential. By generalizing this to the waiting time for a coalescence event in a sample of size , we can write

Each interval

is then approximately exponentially distributed with parameter . The tree depth is the expected time for all lineages to coalesce, i.e. in Figure 11.6). the expected time to the most recent common ancestor (MRCA; It can be computed by applying the rule that the expectation of a sum of random variables is equal to the sum of the expectations:


227

In other words, the expected time to the most recent common ancestor varies by a and . This is the reason why the factor of just between sample size heights of the coalescent trees displayed in Figure 11.5 do not increase much as we increase the sample size by more than an order of magnitude. Note, however, that the above property applies only to the expected time of the . Nothing has been said so far about the random variable MRCA, itself. In fact, it is a sum of exponentially distributed random variables each with a different parameter , if . In order to compute the variance of , are independent. The length of time interval we note that the random variables depends only on the parameters and , but not on time intervals , . Therefore, the variance is (11.1) For large sample sizes we take the limit tion (11.1) and obtain the approximation

of the summation in Equa-

An approximate expression for finite can be found in the following way. We replace the sum on the righthand side in Equation (11.1) by a definite integral ranging from to . We then add a constant, such that for large the limiting values of this integral and the above approximation become identical, and obtain (11.2) We now turn to the computation of the complete branch length, , of the coalescent tree. This is the sum of the lengths of all branches contained in the tree. It is

Again, the expectation of : expectations,

,

, is obtained from the sum of the individual

(11.3) Using Euler’s constant

, this equation can be approximated by (11.4)

228


To compute the variance of , we note that are independent random variables, . Therefore, the variance of is obtained from the individual and so are variances as well: (11.5) For large

we take the limit

The variance for finite

of the summation in Equation (11.5) and find

is approximated by (11.6)

Figure 11.7 shows a plot of the expectation and variance of size . 10

and

vs. sample

)

2

1 0.5

0

10

100

Variance (units of

Expected value (units of

)

1

0.2 1000

sample size ( ) Fig. 11.7. Expectation and variance of (solid) and in units of (left -axis, black), and variance in units of

(dashed). Expectation is plotted (right -axis, gray).

Having discussed coalescent events, we now consider mutation events on the tree. We noted above that mutations accumulate along the branches according to a Poisson


229

distributed random variable with parameter per unit time. Under the infinite sites model this number is identical to the expected number of polymorphisms (or SNPs) , the conditional expectation of the number in an alignment. For fixed time of mutations on the tree, and therefore the number of segregating sites, is

The conditional expectation tation

, which is itself a random variable, has expec. Thus,

(11.7) where the integral is evaluated with respect to the (unknown) probability density of . Inserting the righthand side of Equation (11.3), one obtains (11.8) . Equation (11.8) is due to Watterson [258] and is one of the most where widely applied results in population genetics. , we use the fact that [135, p.9] To calculate the second moment,

Furthermore, using hand side in the above equation can be written as

, the right-

Therefore, Finally, the variance of

is (11.9)

or, in terms of ,

Note that the expected number of segregating sites and its variance are only functions of and , and do not depend on the topology of the underlying tree. In fact, , , any two topologies have for given and given time intervals and therefore the same expected number the same total branch length of mutations. In contrast, tree topology can influence the frequency spectrum of mutations. Consider the two topologies shown in Figure 11.8, each with one mutation.

230

11 Genes in Populations: Backward in Time MRCA

MRCA

Fig. 11.8. Depending on the topology, the frequency spectrum of polymorphisms may change. The left panel shows one singleton mutation, because the mutation (dot) is located on a branch leading to a leaf of the tree. In contrast, in the right panel the mutation is located on an internal branch and, hence, there is no singleton mutation. The combined outer branch lengths are and , respectively. The level of heterozygosity is influenced , in the right as well. In the left topology the average number of pairwise differences is topology it is .

The left panel shows a singleton mutation, as it affects only a single chromosome in the sample. In contrast, the mutation on the right genealogy affects two of the four sampled genes. Singleton mutations are the ones which occur only in a single chromosome in the sample. Singletons can only accumulate on outer branches of the tree. Therefore, the combined branch lengths of all outer branches determines the expected number of singleton mutations in the sample. The two trees in Figure 11.8 have different combined outer branch lengths, yielding different numbers of singleton mutations observed in the sample. In contrast to the number of segregating sites (or SNPs), heterozygosity is a measure of genetic variability which depends on the frequency spectrum of mutations. As we will see in Chapter 12, this fact is exploited to construct statistical tests of the neutral theory. In the step leading to Equation (11.8) we had added mutations to the model and it is instructive to consider the relationship between mutations along the coalescent and the resulting SNP patterns. Mutations affecting many chromosomes tend to have occurred early in the history of the sample as illustrated by the mutations shown as dark gray dots on the left panel of Figure 11.9. In contrast, singleton mutations can only have occurred on an outer branch of the genealogy (Fig. 11.9, left panel, black dots). Any mutation occurring before the most recent common ancestor of a sample leaves no trace in the SNP pattern (Fig. 11.9, left panel, light gray dots). While the action of drift may lead to random fluctuations in coalescent times and tree topology, natural selection leads to systematic changes of these quantities (Fig. 11.9, middle and right panels).

count


6 4 2

6 4 2

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6 4 2

proportion

0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 6 number of pairwise differences 1.0 0.6 0.3

1.0 0.6 0.3

1.0 0.6 0.3 1 2 3

1 2 3

1 2 3

number Fig. 11.9. Top: Gene genealogies and number of pairwise differences for different selection regimes. Displayed are mutations (dots) on the coalescent for sample size and the corresponding SNP patterns. The chromosomes are drawn vertically. Below them are plots of the number of pairwise differences, also called the mismatch distribution, for each panel. Under directional selection (middle panel) average heterozygosity is lower than in the neutral reference case (left panel), because directional selection reduces the number of pairwise differences. In contrast, balancing selection (right panel) maintains two allele classes and thereby leads to an elevated number of pairwise differences and average heterozygosity. Bottom: Frequency spectrum of segregating sites for the three cases. The number of chromosomes in which the rarer of two alleles is found is plotted on the -axis (in this example, only or are possible); the -axis shows the relative frequency among the total number of segregating sites (which is , , and for the three cases, respectively).

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11.6.2 Heterozygosity Let us return to heterozygosity, , the quantity we already discussed in the context of models that move forward in time (Chapter 10). Recall that is the probability that two randomly drawn homologous sequences differ. As we move along the coalescent in from the present into the past, the probability of a coalescence event is . Given each generation, while the probability of a mutation is either one event occurs, the probability that mutation occurs first, thereby creating a pair of distinct alleles, is the ratio

Thus, the random variable with the two states for “coalescence first” for “mutation first” has the distribution and and . Given sample size , is the same for any of pairwise comparisons. For each of the pairwise comparisons there is a Bernoulli ran, with the two states and . dom variable , , but not independent. AverThey are all identically distributed with parameter age heterozygosity is the expected value of the random variable

Since

average heterozygosity is identical to (11.10) We already derived this result in Equation (10.21) using the recursion approach, but from the point of view of the coalescent we obtain an easy alternative derivation of this classical result [142]. it is much harder to derive the variance Due to the lack of independence of since is a function of sample size . Considering the case , the variance is just the variance of the Bernoulli distribution

For large the variance is well approximated by the population variance of This has been obtained by Watterson [257] and is

.

(11.11)

11.8 Recombination

233

11.6.3 The Distribution of Segregating Sites As a corollary to Watterson’s result on expected heterozygosity (Eq. (11.10)) the entire distribution of the number of segregating sites, , can be calculated explicitly for [258] and in recursive form for general . Averaging over coalescence the case times and for one obtains Prob

Prob

The recursion formula for arbitrary Prob

is [120] Prob

where

We finally note that the distribution of segregating sites is well approximated by a Gamma distribution with parameters and (Figure 11.10). These are uniquely determined by the mean and variance of the number of segregating sites (Eqs. (11.8) and (11.9)).

11.7 Simulation Example One of the important features of the coalescent is that it can be implemented as a very efficient simulation tool. Using the coalescent simulation program ms (for make sample) [122], it takes a few seconds to generate 100,000 gene samples of . If we use as input parameter, the expected number of SNPs size according to Equation (11.8) is . The simulated mean of is virtually indistinguishable from this expectation (Fig. 11.10). So far our evolutionary model only encompassed two events: coalescences and mutations. However, at least two other classes of events need to be incorporated to approach a tolerable semblance to evolution in nature: (i) recombination and (ii) selection, the latter being the central tenet of Darwin’s theory of evolution [44].

11.8 Recombination In the coalescent model with mutation, but without recombination, all nucleotides along a given chromosome have the same evolutionary history. However, in sexual eukaryotes recombination takes place in every generation during meiosis. In this

234


no recombination with recombination

0.04

0.03

0.02

0.01

0

0

20

40

60

80

segregating sites Fig. 11.10. The distribution of the number of segregating sites in 100,000 samples of size with . Solid histogram: Neutral coalescent without recombination; outlined . The black vertical line indicates the histogram: including recombination with rate distribution’s mean (35.52), which is almost indistinguishable from the expected mean (35.48). In both cases, with and without recombination, the means of the number of segregating sites are identical, although the distributions differ. The overlayed black and gray lines represent the densities of the approximating Gamma distribution. The simulation was carried out using the freely available program ms [122].

case, different chromosomal segments, e.g. and , may become unlinked and thus have distinct evolutionary histories. Let be the rate per generation with which a recombination event occurs between the two segments in a given chromosome. This rate is proportional to the physical distance between segments and on the chro, where is mosome. To a first order approximation one often assumes that , i.e. in the absence the rate of recombination per nucleotide per generation. If of recombination, the genealogies of and are identical. In contrast, if becomes large, the two genealogies become independent of each other. When tracing the genealogy of two chromosomes back in time, a recombination event may precede the coalescent event (Fig. 11.11). In analogy to the case of mutation, the probability that a recombination event occurs first is

11.8 Recombination

235

where . As before, we wish to calculate the time to the most recent common ancestor, , and the number of segregating sites, . For the two segments, and , there are two, usually distinct, most recent common ancestors (MRCAs), with corand (Fig. 11.11). For the waiting time responding times for one of the two events, recombination or coalescence, is geometrically distributed . Notice that a recombination event with mean increases the number of lineages by one (Fig. 11.11). As we follow the coalescent into the past, further recombination or coalescence events may take place. However, only those recombination events are relevant for the shape of the genealogy, which involve chromosomal segments occurring in the original sample. In contrast, recombination events involving the segments shown as dotted gray lines in Figure 11.11 would leave the genealogy unchanged, as they would not lead to the creation of new lineages. The generalization of the coalescent, which includes recombination, has also become known as the ancestral recombination graph and was introduced by Griffiths and Marjoram [92]. The number of ancestral nodes, , at every point in and time can be described as a birth and death process with rates , respectively. The relative magnitude of these rates ensures the existence of a most recent common ancestor, although there is not only merging but also splitting of branches in a coalescent with recombination. Recombination complicates the explicit derivation of central quantities such as and . However, since recombination splits the coalescent into a number of subtrees, for each of these subtrees the expected total tree length remains identical to the case considered before. Therefore, in the presence of recombination we also have

Note that the argument is identical to the one which has been used above to derive expected heterozygosity (Eq. (11.10)): the expectation is a linear operator. And again for the same reason as above (the variance is not a linear operator), the variance turns out to be more complicated. Kaplan and Hudson [131] derived the following expression (11.12) where is an average of the total sizes of the genealogies of all segments weighted by their lengths. Its variance is

236

11 Genes in Populations: Backward in Time past

sampled chromosomes: segment a

segment b

chromosome not contained in sample:

recombination

x

present

Fig. 11.11. Gene genealogy for sample size and at time .

with recombination separating segments

In the limit of no recombination ( ) collapses to and as a consequence turns into the expression known from Equation (11.9). Furtheris monotonically decreasing in and has the limit . more, Therefore, for the variance of we have . Thus, if all sites along a chromosome are uncoupled, i.e. evolve independently, the number of segregating sites is Poisson distributed. Linkage introduces deviation from the Poisson distribution, although the mutation process along the gene genealogy is still Poisson. However, in the presence of recombination the distribution of the number of segregating sites is still well approximated by a Gamma distribution (Fig. 11.10) with its and . location and scale parameters and uniquely determined by When considering only two segments, and as in the introductory example, an and has analytical expression for the correlation between times been found [131, 120]. It is Cor

(11.13)

It confirms that in the absence of recombination and are completely correlated. Even stronger, Cor if and only if . Notice that the coalescent with recombination is a kind of super-genealogy containing the genealogies of the constituent fragments, in our case and . In Fi-

11.9 Selection

237

gure 11.11 the genealogy is longer than the genealogy. We therefore expect segment to contain a higher SNP density than segment . Genealogies between neighboring segments are uncoupled by recombination and therefore the times to the MRCA for different segments may vary strongly. In apparent contradiction to this observation is the finding that recombination reduces the variance (see Fig. 11.10 and Eq. (11.12)) of the number of segregating sites and thus reduces the variance in genetic variability. However, while recombination introduces additional variation of coalescence times on a local scale, it has an averaging effect on genetic variability on a global scale.

11.9 Selection We have seen that recombination can lead to drastic changes in the coalescent topology. This is also true for selection. In contrast to recombination, selection does not change the branching structure of the coalescent, but it changes the branch lengths. Depending on the mode of selection, time intervals between coalescence events can shrink or expand. Consider a mutation that confers a selective advantage. This mutation and its host chromosome have an increased probability (see Eq. (10.32)) of rising in the population, and may finally become fixed. Given that the mutation does become fixed, its rapid spread through the population, once it has reached a moderate frequency, compresses the coalescent, leading to what is often referred to as a star-like phylogeny. In Figure 11.12 six of the seven coalescence events take place during the spread of the advantageous allele. In contrast, there is only one coalescence event during the subsequent neural phase of evolution. One effect of the star shape of the coalescent with selection is that neutral mutations appear mainly on the outer branches of the genealogy. This implies an excess of singletons or rare polymorphisms compared to what would be expected under neutral evolution. The effects of different types of selection on the shape of the coalescent and, hence, on the distribution of SNPs is illustrated in Figure 11.9. The neutral genealogy shown on the left panel forms the background to all discussions of selection. The kind of selection that favors one allele over another is known as directional selection. The majority of non-neutral mutations are harmful to an organism and are therefore selected against. This type of directional selection is called negative, or purifying, selection. The recurrent removal of disadvantageous mutants from a population is termed background selection [38]. Occassionally, a non-neutral mutant may provide a fitness advantage, for instance when it renders its carrier better adapted to given environmental conditions. This type is called positive, or adaptive, selection. The effect on the shape of the coalescent of such an adaptive mutation in the absence of recombination is shown in the middle panel of Figure 11.9. It causes a star-like phylogeny (cf. Fig. 11.12) that leads to an excess of singleton mutations. A different scenario is created by the existence in the population of two alleles that are beneficial only if present at the same time. A standard example for such genetic synergism is sickle-cell anemia, where the combined presence of the sickle allele of -globin and its wild type confers heightened malaria resistance [106, p.

238

11 Genes in Populations: Backward in Time 0

0.5

1.0

neutral

neutral

past

present

Fig. 11.12. Genealogy with selection (sample size ). A positively selected mutation, , (dot) gets fixed in the population and thereby dominates the genealogy of the sample in the present. The frequency of the selected allele, , is plotted on the righthand side. The dotted lines indicate the time interval with an elevated rate of coalescence events while allele rises quickly to fixation, also called a “selective sweep”. The sweep is flanked by two phases of neutral evolution.

230]. The resulting balancing selection leads to deep branching in the coalescent and correspondingly a bimodal distribution of pairwise differences as shown in Figure 11.9 (right panel). In summary, for a given sample of homologous DNA sequences drawn from a panmictic population, the number of SNPs can easily be established. Coalescent theory makes predictions about distributional properties of the number of segregating sites under diverse evolutionary scenarios. These theoretical predictions can be compared to experimental observations. The number of evolutionary processes that can be incorporated in coalescent models is only limited by a researcher’s ability to implement the corresponding software. The widely used program ms incorporates reciprocal recombination as well as gene conversion, migration between subpopulations, and changes in population size over time [122].

11.10 Combining Recombination and Selection Coalescent models that combine selection and recombination can be used to determine the influence of a selected allele on the number and distribution of polymorphisms in its chromosomal neighborhood. With low rates of recombination, the fate

11.10 Combining Recombination and Selection

239

of this neighborhood is tightly coupled to the selection dynamics (see Fig. 11.9), whereas with large recombination rates it is essentially unaffected by the kind of selection upon neighboring alleles. 1.0

0.5 MRCA

0.0 past

present

Fig. 11.13. “Hitchhiking” with a selected allele. The genealogy of the neutral alleles ,..., depends on whether or not recombination takes place during the fixation of allele . Without the recombination event ( ), the MRCA of the alleles in the sample would be much younger, for example at time , because in that case all alleles would be descendants of a -carrying chromosome. The frequency of allele , , is indicated by the bold line.

We now concentrate on the interaction of recombination and positive directional selection. In this case, the neighborhood of the selected allele can be regarded as hitching a ride to fixation with the adaptive trait. Hence, this evolutionary scenario is also known as genetic hitchhiking [174, 227]. The effect of hitchhiking depends strongly on recombination during the selected allele’s rise to fixation as illustrated in Figure 11.13. Recombination leads to an uncoupling between a selected allele and other portions of the chromosome. Conversely, in the absence of recombination any episode of directional selection at any gene along a chromosome would impose a star phylogeny on all these genes. Such a star phylogeny is characterized by low levels of diversity (cf. Fig. 11.9, directional selection). This is the reason why it is difficult to reject neutrality on the basis of finding the supposedly low genetic diversity of in E. coli [182] as discussed in Section 10.6. This bacterium reproduces asexually and has essentially a clonal population structure [222]. Due to periodic selection such a population structure leads to a genome-wide reduction in genetic diversity even in very large populations. How strong the effect of periodic positive selection is on the levels of genetic diversity depends on its frequency and intensity [164]. In the presence of recombination genetic diversity may also be reduced due to recurrent episodes of positive selection. However, the effect depends additionally on

240


0.012

autosomal X−linked

0.008

0.004

0

0

4e−09

8e−09

1.2e−08

Fig. 11.14. Scatter plot of genetic diversity per bp ( ) versus recombination rate ( ) for twenty loci from Drosophila melanogaster (Table 11.2). Genetic diversity tends to be lower in regions of low recombination. Linear regression analysis yields a correlation coefficient of for these data (straight line). The curved line is obtained by fitting the parameters , and of the equation to the data [260]. The quantity is the equilibrium level of genetic diversity per bp under neutral evolution. Population size was estimated independently ( ). For the data shown, the estimates are , and .

the magnitude of the regional recombination rates [132, 236, 20]. In fact, as a result of recurrent positive selection, one expects to observe a positive correlation between the recombination rate and genetic diversity. Figure 11.14 shows measurements of recombination rates and genetic diversity which were collected for a set of loci in Drosophila melanogaster (Table 11.2). Genetic diversity refers here to average heterozygosity per bp, which is commonly abbreviated by the letter . The positive slope of the regression line shown in Figure 11.14 confirms this expectation. Given that selection was the driving agent to produce the positive correlation of and and given a model of the hitchhiking effect, more information can be extracted from such data. In one model [260] an explicit relationship between genetic diversity and the recombination rate has been derived, where quantities such as the selection coefficient, , and the rate of selected substitutions, , are contained as parameters. Fitting

11.10 Combining Recombination and Selection

241

Table 11.2. Measurements of recombination rate per bp ( ) and genetic diversity per bp ( ) for twenty gene loci in Drosophila melanogaster. locus

reference

X-linked loci Yellow,Achaete Pgd Z,Tko Per White Notch Vermilion Forked Zw Su(F)

0.429 1.466 2.114 4.952 13.30 11.54 5.619 4.330 4.619 0.476

0.0008 0.0030 0.0044 0.0014 0.0090 0.0050 0.0010 0.0020 0.0007 0.0000

[19] [19] [4] [19] [183] [216] [20] [160] [53] [160]

autosomal loci Gpdh Adh Ddc Amy Pu Est6 MtnA Hsp70A Ry Ci

5.714 4.621 1.314 3.107 5.129 4.314 0.593 0.493 3.364 0.000

0.0078 0.0060 0.0050 0.0080 0.0040 0.0050 0.0010 0.0020 0.0030 0.0000

[241] [161] [20] [162] [241] [84] [159] [163] [12] [21]

In order to compare autosomal and X-linked data they need to be standardized. Since in Drosophila recombination takes place only in female flies, has to be multiplied by for autosomal loci and by for X-linked loci. To compensate for the differences in effective population sizes for X-linked and autosomal loci, from X-linked loci needs to be multiplied by . The standardized data are plotted in Figure 11.14.

these parameters to the experimental data, the curved graph of versus shown in Figure 11.14, emerges. A parameter fitting procedure yielded estimates of and per nucleotide per generation for these data [260]. That is, on average a selected allele has a three percent better chance of being passed on to the next generation than its neutral homologue. Such analyses shed light on the old question regarding the selective value of molecular polymorphisms. In contrast to the early phase of the selectionists vs. neutralists controversy, this debate is today informed by a wealth of molecular data on sequence polymorphisms seen within species and populations. Based on these data, hitchhiking models are now widely used to trace individual selective sweeps in a genome in addition to quantifying an overall rate of positively selected substitutions. This area of research, with the scope of identifying

242


those sites in a genome which currently are, or recently have been, under adaptive evolution, is known as hitchhiking mapping [103, 217, 188]. Together with a set of tests for neutrality, which is the topic of Chapter 12, the statistical significance of putatively non-neutral regions in a genome can be established.

11.11 Summary In this chapter we have turned the Wright-Fisher model of gene dynamics on its head. Given a sample of genes, their lineages coalesce into common ancestors as we move backward in time. If we trace back the gene genealogies until the most recent common ancestor of the entire sample, this sample genealogy is called the coalescent. Coalescent theory allows the derivation of important evolutionary quantities such as the number of segregating sites expected under neutrality. In addition, the coalescent is routinely used as a very efficient tool for simulating gene samples under a diverse range of evolutionary scenarios, including recombination and selection.

11.12 Further Reading A classical review of coalescent theory is written by Hudson, one of the founders of coalescent theory [120]. More recently a number of surveys of this approach to evolutionary modeling have been published, the most accessible of which is perhaps an article by Nordborg [192]. In addition, Hein, Shierup, and Wiuf provide an introductory monograph on coalescent theory [112].

11.13 Software Demonstrations and Exercises 11.1. The bioinformer software contains under Evolution Coalescent a program that visualizes neutral coalescent trees with mutation and the corresponding SNP patterns. Start this program and work through the corresponding tutorial in Section A.4.4. WrightFisher 11.2. The bioinformer also contains under Evolution a program that visualizes ancestral lineages in a Wright-Fisher population. Work through the tutorial for that program in Section A.4.3. 11.3. How many SNPs do you expect under the neutral infinite sites model in a samgenes given ? ple of to get an What is the sample size needed in a haploid population of size average time to the most recent common ancestor of 10 generations? 11.4. Use the freely available coalescent program ms [122] to simulate 10,000 gene and . samples given the parameters

11.13 Software Demonstrations and Exercises

243

1. How long does the simulation take? 2. What is the mean and variance of the number of SNPs in the simulated samples? 11.5. Repeat the simulation, but this time include reciprocal recombination with and . 1. How long does the simulation take? 2. What is the mean and variance of the number of SNPs in the simulated samples? 3. Compare your results to the simulations without recombination. 11.6. In coalescent genealogies with recombination there is splitting and merging of lineages. Do all coalescent genealogies with recombination have a most recent common ancestor?

sdfsdf

12 Testing Evolutionary Hypotheses For many years population genetics was an immensely rich and powerful theory with virtually no suitable facts on which to operate. Richard C. Lewontin [166, p. 189]

Experimental data such as the eleven Adh sequences of D. melanogaster summarized in Table 10.1 represent a single realization of an evolutionary process. In biology, such data cannot be arbitrarily replicated since—in most cases—the evolution experiment conducted by nature over long periods of time cannot readily be repeated. This begs the question of how data of a single realization of a stochastic process can be evaluated and interpreted. The answer is given by a number of statistical tests, which are designed to compare scarce data with a probability distribution and to ask whether or not the data are extreme with respect to the distribution. Such probability distributions can sometimes be obtained analytically and one example of this is Ewens’ sampling formula introduced in Section 10.7. However, in most cases they are generated by computer simulations of the evolutionary process. Out of the multitude of methods that have been developed over the years to test various evolutionary hypotheses, we are going to concentrate in this chapter on examples from two areas: tests of the neutral mutation hypothesis and tests of recombination.

12.1 Hudson-Kreitman-Aguadé (HKA) Test According to the neutral mutation hypothesis, the genetic variability between populations should be correlated with the variability within populations. The neutrality test by Hudson, Kreitman, and Aguadé, also known as the HKA test, assesses this loci in two species that form prediction [124]. It requires sequence data from the basis for three statistics: : number of segregating sites at locus in species , i.e. the within-species diversity at locus ; 2. : number of segregating sites at locus in species ; : number of nucleotides at which two randomly selected sequences at locus 3. from species and differ, i.e. the between-species divergence at locus .

1.

The test is based on the following assumptions:

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12 Testing Evolutionary Hypotheses

1. discrete generations; 2. mutations are neutral, independent, Poisson-distributed, and non-recurrent (neutral infinite sites model); 3. there is no recombination within loci; 4. all loci are unlinked, i.e. in linkage equilibrium; , and , respectively; 5. species and have reached a constant size of have diverged from a common ancestral population gen6. species and erations ago; the size of this ancestral population was the average of the two . species’ present population size, i.e. Given this set of rather conservative assumptions, expectations ( ) and variances (Var) for the central quantities of the test can be derived: (12.1) Var Var where , is the size of the sample drawn from species , the neutral mutation parameter of a diploid population, the population size, the number of mutations per generation at locus , , the harmonic number,

and

the harmonic number of order :

The system of Equations (12.1) contains the unknown quantities , , and . Their estimators, , , and , were obtained by solving the following system of equations: (12.2)

12.1 Hudson-Kreitman-Aguadé (HKA) Test

247

By replacing the parameters in Equations (12.1) with their estimators, we get estimators for the central quantities used in the test. Assuming that the within population diversity and the between population divergence are normally distributed, the distributed [231]: following test statistic is approximately

Var

Var

Var

To see how this works, let us take a look at the application given by the authors of the HKA test [124]. Table 12.1 summarizes their diversity data from two fruitfly species, D. melanogaster and D. sechellia. The data were obtained using restriction length polymorphisms. Since restriction enzymes only sample a subset of the positions in a target sequence, Table 12.1 draws a distinction between the lengths of the sequences surveyed and the number of sites compared. Moreover, it is clear that the Adh locus and its 5’ flanking region are linked, thereby violating one of the assumptions of the test. However, the authors showed that the test is conservative with respect to its assumption of no linkage between loci. That is, if the loci tested are linked, it becomes more difficult to reject the null hypothesis of neutrality and the same applies to the assumption of no intra-genic recombination [124]. Table 12.1. Restriction fragment length polymorphism (RFLP) data to illustrate the HKA test: distribution of variable sites (Var. sites) around the alcohol dehydrogenase (Adh) locus in D. melanogaster and between D. melanogaster and D. sechellia. Data taken from [124]. 5’ Flanking

Within species ( Between species

Adh locus

Length Sites Var. sites Length Sites Var. sites ) 4000 414 9 900 79 8 4052 4052 210 900 324 18

Let refer to the per nucleotide neutral mutation parameter, in contrast to the used so far, which refers to the per locus neutral mutation parameter. Further, we identify species with D. melanogaster and species with D. sechellia. By substituting the data on the distribution of genetic variation from Table 12.1, the top equation in system (12.1) describing the within-species diversity becomes (12.3) There was no data available on within-species diversity. As a substitute it was simply assumed that the ancestral population of species had the same size as that of . The species , i.e. that in the third and fourth equations of system (12.1)

248


remaining equations of system (12.1) describing between-species diversity and connecting within- and between-species diversity therefore become (12.4)

Solving Equation (12.3) and system (12.4) yields , , , and . This -value corresponds to a -value of , distribution with 1 degree of freedom; in other words, the data did not assuming a agree with the neutral mutation hypothesis. In order to discover the kind of evolutionary force that might have caused this rejection of the neutral mutation hypothesis, it is necessary to reconsider the experimental data (Table 12.1). There was an approximately equal amount of divergence and ). In contrast, the diverbetween species ( sity within species was approximately four times higher at the Adh locus than in the vs. ). The authors therefore conclude flanking region ( that balancing selection is likely to affect the coding region of the Adh locus in fruit flies [124].

12.2 Tajima’s Test As we have seen, the HKA test requires polymorphism data from two species. In contrast, Tajima’s test of the neutral mutation hypothesis is based on a sample of sequences drawn from a single population [240]. Consider an alignment of homologous sequences. For each of the possible pairs of sequences we can count the number of positions at which they differ. The arithmetic average of this number is and defined as . This is an estimator of the neutral mutation parameter we define Like the average number of pairwise differences, the number of segregating sites is . We therefore also a function of as well as of the sample size : define a second estimator of :

Based on these two estimators of , Tajima suggested the test statistic

Var

(12.5)

12.2 Tajima’s Test

249

Under strict neutrality and, hence, . The denominator ensures that the standard deviation of is . behaves under non-neutrality, notice that as long as the To understand how number of alleles in a sample is constant, is independent of allele frequencies. This leads to a disproportionately large contribution of rare alleles to . Since deleterious . is therefore alleles are rare, such alleles lead to a situation where indicative of purifying selection. Alternatively, if a gene is fixed through rapid selection of an advantageous allele, neutral genetic diversity linked to the advantageous variant is also swept to fixation. Such a phenomenon is known as a “selective sweep” or “genetic hitchhiking” [174] and is characterized by an excess of low-frequency alrelative to leles in the sample (cf. Section 11.10). This also leads to an inflation of and, hence, to a negative value of Tajima’s . Conversely, alleles that grow from low to intermediate frequencies increase while leaving unchanged. A value of therefore indicates balancing selection. In order to actually apply Equation (12.5), we still need to estimate the statistic’s denominator. This is obtained from equations given by Tajima [240]: Var

As an example application for Tajima’s we return to the FOXP2 gene introduced in Section 9.8.1. A single amino acid replacement in the DNA-binding domain of the FOXP2 protein causes severe articulatory difficulties as well as linguistic and grammatical impairment in humans [63]. In a survey of this gene, 14,063 nucleotides were sequenced from 40 human FOXP2 genes. Table 12.2 shows the 47 polymorphic sites detected, which occurred in 17 unique haplotypes. The dominant haplotype accounted for over half of the sample (21 copies), three other haplotypes occurred twice, and the remaining 13 were unique [63]. Figure 12.1 shows the frequency and distribution of the 47 SNPs along the region surveyed. Two-thirds (31) of the polymorphisms occurred just once, which means they were generated by mutations that affected either a single, or, though less likely, 39 genes in the sample. This excess of compared to rare variants is reflected in the inflated value of and a correspondingly small [63]. This is certainly less than zero, but is this deviation from neutral expectation significant? There are two methods for assessing the statistical significance of a given value of : by assuming that follows a known null distribution, or by simulating this null distribution. In the original publication of the test, Tajima suggested that the null distribution of is approximated by the beta distribution. However, the application of assumed null distributions for has been criticized [81] and coalescent simulations are often used instead. The results of such simulations for the FOXP2 data are shown in Figure 12.2. The neutral coalescent was used to generate 100,000 artificial data sets conditioned on the sample size and the observed genetic diversity. From

250


Table 12.2. The 17 unique haplotypes detected in a survey of 14,063 nucleotides at 40 human FOXP2 loci. A dot indicates a match to the nucleotide in the top line. Count gives the number of copies of the respecitve haplotype. Adapted from [63]. # Count Haplotype 1 21 AAATAGAAACACCAGAGAGAGACGTCCGATACGTGCTATAATGATCG 2 2 ..............A....................T........... 3 2 ..G............................................ 4 2 ...G..........A.......T..TA..A..........G...C.A 5 1 .......................A....................... 6 1 ..............A................................ 7 1 ..............A.........C..A..............A.... 8 1 .......................A.....................T. 9 1 C..G.AGT...TTGA....TA.......CA..CC..A.GT...G... 10 1 ..............................G................ 11 1 ........G.....A......G.............T........... 12 1 ........G.....A....................T........... 13 1 ...G..........A.......T..TA..A.T........G...C.A 14 1 ....G..........G............................... 15 1 ...G......G...A.AGC......TA..A....TT.....C..... 16 1 .T............................................. 17 1 .........T....A...C...............TT.C...C.....

12 11 10 9

Count

8 7 6 5 4 3 2 1 0 0

1000

2000

3000

4000

5000

6000

7000

8000

9000 10000 11000 12000 13000

Nulceotide Position

Fig. 12.1. Position and count of the rare allele at each single nucleotide polymorphism (SNP) along the 14,063 nucleotides surveyed at 40 human FOXP2 loci. The position of the first SNP is set to 1. Adapted from [63].

12.3 Fu and Li’s Test

251

each of these data sets was calculated and the resulting null distribution of has implies a mean close to 0 (-0.06), as expected. The observed value of , which agrees with the hypothesis that FOXP2 was affected by a that recent selective sweep [63].

3000

Mean

Count

2000

Observed 1000

0 -3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

Tajima’s Fig. 12.2. Testing neutrality at the FOXP2 locus in humans. Distribution of 100,000 values of Tajima’s calculated using the neutral coalescent with and , as observed in the FOXP2 sample shown in Table 12.2. The position of the arrow indicates that under a neutral model of evolution the observed value of is highly unlikely ( ). Coalescent simulations were carried out using the software ms [122].

12.3 Fu and Li’s Test The test of neutrality due to Fu and Li is similar in spirit to Tajima’s test statistic, but its justification is based squarely on coalescent considerations. The central idea of the test is that under neutrality the number of mutations on external branches of a genealogy is equal to the number of mutations on its internal branches. Since deleterious mutations tend to have arisen recently in a sample’s evolutionary history, they should

252


accumulate on the external branches of the coalescent. As illustrated in Figure 12.3A, such evolutionary events result in singleton mutations. The converse, however, is not A

1

2

B

3

4

5

1

1

1

2

2

3

3

4

4

2

3

4

5 Fig. 12.3. Relationship between the topology of a gene genealogy, the distribution of mutations ( ) on this genealogy, and the resulting pattern of single nucleotide polymorphisms (SNPs). A: On a topology where neither of the two branches joined at the root are external, all singleton mutations ( ) must have arisen on one of the genealogy’s external branches. B: If one of the two branches leading to the root is external, mutations arising in the two branches and both result in singleton SNPs.

always true. In order to see that not every singleton has necessarily arisen on the external branches of a coalescent, consider the genealogy shown in Figure 12.3B. Any mutation along branch will cause a SNP pattern that is indistinguishable from a singleton mutation. This constellation is not possible, if neither of the two branches leading to the genealogy’s root is external, as is the case in Figure 12.3A. Taking this into account, Fu and Li suggested the following test statistic: (12.6) Var where is the number of singleton mutations. The variance of the numerator of Equation (12.6) is obtained from the equations given by Fu and Li [81] as Var where

12.5 Minimum Number of Recombination Events

253

The FOXP2 data set we have already investigated using Tajima’s (Table 12.2) sequences and polymorphisms, of which are contains , singletons. Substitution of these numbers into Equation (12.6) yields which is significant according to the critical values provided by Fu and Li ( ) [81]. This is in good agreement with the result we obtained using Tajima’s .

12.4 McDonald-Kreitman Test Like the HKA test, the McDonald-Kreitman test is based on sequence data from at least two species [178]. However, in this case a single locus suffices, which needs to be protein-coding. First, all nucleotide positions in such a data set are classified as fixed differences or polymorphisms. A position represents a fixed difference, if it is monomorphic within but polymorphic between the two species investigated (Fig. 10.3B). A position is polymorphic, if it is variable within either or both of the species. Next, the fixed and polymorphic positions are classified into synonymous and non-synonymous substitutions. Under the neutral mutation hypothesis, sequence changes accumulate at a constant rate along branches, regardless of whether they lead to species or to individuals within species. This implies that the ratio of synonymous to non-synonymous substitutions should be equal between fixed and polymorphic positions. That is,

where and refer to the numbers of non-synonymous and synonymous fixed and denote the number of non-synonydifferences, respectively. Similarly, mous and synonymous polymorphisms. McDonald and Kreitman applied this test to the alcohol dehydrogenase gene (Adh) of D. melanogaster, D. simulans, and D. yakuba, whose substitution data is summarized in Table 12.3. Such a table is tested using Fisher’s exact test [231]. On . the basis of this test, the authors rejected the null hypothesis that In other words, there is a significant excess of fixed differences between the three species. The authors conclude that this is due to adaptive fixation of selectively advantageous mutations [178]. This conclusion agrees with that drawn in Section 12.1 by applying the HKA test to the same locus.

12.5 Minimum Number of Recombination Events The neutrality tests described so far were all based on the assumption that there has been no recombination within the loci investigated. We now look at a procedure for assessing the validity of this assumption.

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Table 12.3. Polymorphism data for the alcohol dehydrogenase gene (Adh) of D. melanogaster, D. simulans, and D. yakuba. Data taken from [178].

Non-synonymous Synonymous

Fixed differences Polymorphisms 7 2 17 42

Figure 12.4 shows a pair of chromosomes that differ at two positions along their DNA sequences. Hence, these chromosomes form two distinct haplotypes, AA and TT. If reciprocal recombination takes place between the two polymorphic loci, two new haplotypes are formed, AT and TA, leading to a total of four haplotypes. Since under the infinite sites model any position is assumed to mutate only once, the detection of all four possible haplotypes between a pair of single nucleotide polymorphisms is evidence of a recombination event. Counting the number of four-allele pairs of nucleotides should therefore give us an indication of the number of recombination events that have taken place in the evolutionary history of a sample of DNA sequences. However, there are two complications to consider. First, we have no guarantee that representatives of all four haplotypes generated by a recombination event actually end up in our gene sample. The number of four-allele combinations is therefore going to lead to an estimate of a minimum number of recombination events, [123]. Second, not all pairs of loci that display four alleles are evidence of a recombination event.

A

A

A

A

A

T

T

T

T

T

T

A

Fig. 12.4. Reciprocal recombination between two polymorphic sites generates the haplotypes AT and TA out of the original pair of haplotypes AA and TT.

In order to calculate from a set of aligned DNA sequences with phic sites, we can apply the following algorithm [123]:

polymor-

1. Construct a matrix that contains for each pair of polymorphic posian entry of if the pair forms all four possible haplotypes and tions otherwise. each pair with is interpreted as an 2. In the upper triangle of open interval and a list of such intervals is formed, where . 3. Delete from the list all intervals that are subintervals of another interval. , starting at and continuing to 4. Delete all intervals that overlap with .

12.6 Detecting Linkage Disequilibrium

5.

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is the final number of intervals in the list.

If , we know that recombination has taken place. However, we cannot because, as noted already, our sample conclude no recombination from of DNA sequences might not contain all four haplotypes generated by reciprocal recombination.

12.6 Detecting Linkage Disequilibrium We have seen that the HKA-Test (Section 12.1) is not only based on the assumption of no within-locus recombination, but also that the loci investigated are not linked. There are two meanings of the term “linkage” between two or more loci. First, it might refer to the fact that two loci occur on the same chromosome. Second, it might refer to the fact that two loci are not statistically independent. In the context of hypothesis tests this probabilistic interpretation is usually applied. Our investigation of and . The its implications starts with the simplest case of two bi-allelic loci, probability of finding allele “1” at locus is denoted by and at locus by . , and . The frequencies of the four possible haplotypes are denoted Our measure of linkage disequilibrium is then defined as

which is zero in case of linkage equilibrium. For more than two alleles, all pairs of alleles need to be considered. If more than two loci are investigated, all pairwise comparisons between loci need to be made. This expansion of pairwise comparisons quickly becomes cumbersome, and a summary statistic, the index of association ( ), was developed to measure linkage between an arbitrary number of alleles at an arbitrary number of loci [29]. The index of association is based on the number of pairwise differences between a set of haplotypes. The computation of such pairwise differences is illustrated in Fidenote the observed variance of this number and its expectation gure 12.5. Let under the null hypothesis of linkage equilibrium. At linkage equilibrium and, hence, might be a useful measure of linkage disequilibrium, which scales linearly with the number is zero at linkage equilibrium [29]. However, of loci analyzed [121]. We therefore define the standardized index of association as [108]

The value of

where

can be estimated from the data as

is the probability of drawing two distinct alleles at locus ,

256


1 2 3 1 1 2 2

1 2 2 2

1 2

1

-

1 1 1 2

Fig. 12.5. Computation of the index of association ( ). Starting from haplotype data for a number of organisms ( ) at a number of loci ( ), the matrix of pairwise mismatch values is calculated as well as the genetic diversity at each locus ( ).

and where is the frequency in the sample of allele at locus . can be assessed either by simulation or The significance of a given value of by assuming that under linkage equilibrium is normally distributed. The simulation proceeds by resampling the alleles at each locus without replacement, thereby , is unlinking the loci [234]. From each resampled data set a new value of . This calculated and the desired significance is the frequency of observing , where is the number of resamplings and the procedure has a run time number of haplotypes sampled. The alternative test is based on the assumption that is normally distributed. Given this scenario, a formula under linkage equilibrium needs to be computed once in time [109]. This parafor the variance of metric test is one-sided, like the test based on simulations. The reason for this is that any deviation from the null hypothesis of linkage equilibrium leads to an inflation of compared to . The index of association is most popular among microbiologists, because it is easy to obtain the haplotype data on which the test is based from bacteria. In microbiology the application of the index of association has lead to the discovery that bacterial populations have structures ranging from strictly clonal in Escherichia coli to panmictic in Neisseria gonorrhoeae [175]. However, the test has also been applied to eucaryotes, for example to investigate the population structure of wild barley, Hordeum spontaneum [29], or of thale cress, among molecular biologists better known as the model plant Arabidopsis thaliana [225].


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12.7 Implementations With the exception of the index of association, the tests discussed in this chapter have been implemented in the software DnaSP [211]. This is a freely available program with a user-friendly graphical interface. The index of association has been implemented in the software LIAN, which is also freely available [108].

12.8 Summary Since the inception of the neutral mutation hypothesis in the 1960s, a large number of tests have been devised that assess the validity of various evolutionary scenarios. In this chapter we have covered four tests of neutral evolution, the Hudson-KreitmanAguadé (HKA) test, Tajima’s , Fu and Li’s test, and the McDonald-Kreitman test. The first three of these are based on arbitrary DNA polymorphism data. Tajima’s as well as Fu and Li’s test have the pleasing property that the direction of selection can be read from the sign of the test statistics. In addition, the data for these tests are drawn from a single population, while the HKA test is based on data from two species. Similarly, the McDonald-Kreitman test is based on DNA sequences obtained from two or more species. However, in the case of the McDonald-Kreitman test the input sequences need to be protein coding. All four tests of neutrality described in this chapter assume that there has been no within-locus recombination in the history of the sample. This assumption can be investigated by computing a minimum number , based on the four-haplotypes test. If we know of recombination events, that recombination has taken place, while the converse inference cannot be drawn. In addition to the assumption of no within-locus recombination, the HKA test also assumes that the loci investigated are in linkage equilibrium. A convenient measure of linkage disequilibrium between an arbitrary number of loci with two or more alleles is the index of association, .

12.9 Exercises and Software Demonstration 12.1. Calculate the harmonic numbers

and

.

. What is the number of external and internal 12.2. Draw a genealogy for branches on that genealogy? What is the general number of external and internal branches in genealogies describing the history of sequences? 12.3. Use the bioinformer-Program Evolution Coalescent to familiarize yourself with the relationship between the genealogy of a sample of genes, the distribution of mutations on this genealogy, and the resulting observable polymorphism pattern. 12.4. Here is a stylized haplotype data set:

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1 2 3 4 5 Draw a possible genealogy that explains this data set. 12.5. Draw an alternative polymorphism pattern that might be generated by the genealogy just uncovered. 12.6. Draw an alternative genealogy that could generate the haplotype pattern shown in Exercise 12.4. 12.7. Given the two haplotypes AG and TC, what are the haplotypes generated by reciprocal recombination between the two SNPs? 12.8. Consider the following DNA sequence data AGTCGA AACCCC TGTTCA TACTGC and calculate its

.

12.9. Consider the following multilocus data set 1 1 2 1

1 1 2 4

3 3 3 5

2 2 2 7

and calculate its standardized index of association,

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A bioinformer

bioinformer is an integrated collection of demonstration programs for visualizing a number of the concepts and data structures treated in this book. The following sections give some background to each of the demos, describe the program, and conclude with a tutorial.

A.1 Alignment A given sequence whose function is unknown can often be annotated by finding a similar sequence of known function among the large number of sequences deposited in public data bases. Searches for sequence similarity are usually based on alignments. The following programs concentrate on pairwise alignment through simple dynamic programming and protein substitution matrices. A.1.1 Protein Substitution Matrices Pairs of residues in DNA alignments are traditionally scored by distinguishing only between matches and mismatches. This works reasonably well for DNA sequences but is not applicable to proteins. For a start, the number of mutational steps needed to convert a codon for one amino acid into that for another varies between one and three. In addition, amino acids differ markedly in their physico-chemical properties (Fig. C.4). As a result, amino acid substitutions have highly variable effects on the structure of the protein affected. Since most changes in a protein’s structure are deleterious, selection will filter out substitutions with drastic structural effects. Finally, substitution probabilities change over time, making it necessary to construct series of substitution matrices to cover a range of evolutionary time scales. Substitution log-likelihoods of observing a homologous matrices summarize the pair of amino acids (cf. Section 2.4).

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Description of Program The program displays the PAM and BLOSUM matrices. On the PAM tab PAM matrices are provided for evolutionary distances ranging from 2 PAM to 500 PAM. The user selects the PAM number through a slider which can be manipulated using either the mouse or the and keys. The entries of PAM matrices are expressed in bits and by convention different bit fractions have been used to scale the majority of matrix entries to single digit integers. The user can therefore choose from a range of such scale factors. In addition, the program displays the expected percentage of mismatched residues between proteins separated by an evolutionary distance of PAM. The implemented calculations are based on the original data [46] and, hence, the matrices may differ in numerical detail from the versions currently available, e.g. as part of the BLAST distribution [10]. On the BLOSUM tab three commonly used BLOSUM matrices can be displayed: BLOSUM45, BLOSUM62, and BLOSUM80. Tutorial 1. Look at the PAM matrix displayed when the program is first launched (use the reset button if necessary) and notice that the values along the main diagonal, which represent match scores, are all positive. This contrasts with the off-diagonal entries, which represent mismatch scores. These are on average negative. Identify the two amino acids with the highest entries on the main diagonal, i.e. the two most conserved amino acids. Can you make biological sense of their high degree of conservation? 2. Slide the PAM number to small values. Notice that the expected difference is now equal to the PAM number. This is because, say, two mutations per 100 amino acids will on average have caused a 2% difference in the corresponding pairwise comparison. 3. Slide the PAM number to very high values. Notice three things: a) The PAM number now vastly exceeds the Expected difference. This is because mutations can hit a given position more than once and this occurs with increasing frequency the more mutations are thrown randomly at a segment of 100 amino acids. At a more basic level, percentages vary between 0 and 100, while the number of mutations per 100 residues (PAM ) can range between 0 and infinity. b) The entries in the matrix tend towards 0. The reason for this is that the probability of finding the corresponding pair of residues due to homology becomes in the long run equal to the probability of finding the pair by chance. . The ratio of two equal numbers is 1 and c) A few amino acids retain high entries on the main diagonal even for high PAM values. Identify the two amino acids with the highest entries. 4. Compare the entries between PAM160 and BLOSUM62 for amino acid pairs histidine/alanine, valine/valine, and tryptophan/tryptophan. Do you notice any differences?

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A.1.2 Number of Alignments An alignment can end in three different ways: n n - n n where n indicates any residue. These three possibilities suggest the following recursion for the number of possible global alignments [253]:

where is the number of possible alignments between two strings of length and . If we further define the boundary condition that there is a single alignment between a sequence of any length and a sequence of length zero, we are in a position to compute the number of possible global alignments (cf. Section 2.5). The possible number of local alignments is the sum of the number of global alignments for all possible pairs of substrings. A sequence of length contains substrings of length . Consider as an example two sequences of length two each. Nine pairs of substrings can be formed between these. Four of the substring pairs have length (1,1), yielding three possible global alignments each; another four have length (2,1) or (1,2), yielding five possible global alignments each; one has length (2,2), yielding 13 possible global alignments. The number of possible local . alignments is therefore Description of Program The program calculates the number of different global or local alignments that can be constructed for two sequences of lengths between 1 and 400. The user can choose the lengths of the two sequences as well as the alignment mode (global or local). Tutorial 1. Contemplate the number of global alignments that can be formed between two sequences of lengths 339 and 320. 2. Compare that number to the number of molecules contained in a liter of water: . How much water is needed to accumulate the same number of water molecules as there are possible alignments between two sequences of lengths 339 and 320? 3. Compute the time it would take to list all possible global alignments between two sequences of length 320 given that the calculation of a single alignment seconds. takes, say, 4. Compute the time it would take to list all possible local alignments for two sequences of length 320.

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A.1.3 Pairwise Alignment Similar sequences tend to encode similar functions. The search for sequence similarities is therefore the bread and butter of computational biology. This program is concerned with alignment by dynamic programming, which was pioneered for global alignment by Needleman and Wunsch [189]. The local alignment algorithm is due to Smith and Waterman [230]. Description of Program This program visualizes the dynamic programming matrix used for computing optimal pairwise alignments between two sequences over an arbitrary alphabet. It also visualizes the path through the matrix that corresponds to an optimal alignment. In addition, it displays the alignment, its score, and calculates the number of cooptimal global alignments. Three alignment modes are implemented: 1. global: sequences are assumed to be homologous over their entire length (Section 2.6); 2. overlap: sequences are assumed to have overlapping ends (Section 2.7); 3. local: sequences are assumed to possess only local homology (Section 2.8). The user can enter any sequence and manipulate the following parameters: 1. match score, 2. mismatch score, 3. gap extension. Notice that most alignment algorithms implement an affine gap scoring scheme, i.e. the total gap cost, , is where is the gap opening cost, the gap extension cost, and the length of the gap. In this program the gap opening cost is ignored, i.e. set to zero. Tutorial 1. Choose the global alignment algorithm. 2. Click on >> to set up the dynamic programming matrix corresponding to the sequences entered. 3. Click again on >> to initialize the dynamic programming matrix. 4. Click on >> to fill in the dynamic programming matrix. Verify that the entry in cell A A has the expected value. 5. Click one final time on >> to carry out the traceback. The corresponding alignment and its score is shown at the bottom of the panel. In addition, the number of cooptimal alignments is displayed. These are alternative paths through the matrix carrying the same optimal score. There is no algorithmic criterion for distinguishing between these alternatives. 6. Repeat the above steps using the local alignment algorithm. 7. Repeat the above steps using the overlap alignment algorithm.

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A.2 Match Operations based on string matching are an integral part of sequence analysis. The following programs concentrate on exact matching with and without suffix trees. A.2.1 String Matching At its most basic, string matching consists of looking for a short pattern in a large text. This is a classical topic of computer science [99]. However, string matching methods are ubiquitous in computational biology, since a core concern of the field is the interpretation of genetic sequence texts [99]. Description of Program The program implements three methods of string matching: 1. Na¨ıve string matching (Section 3.2), which essentially consists of two nested do loops, the outer running over the text, the inner over the pattern. Accordingly, the worst case run time of this algorithm is , where and are the lengths of the pattern and the text, respectively. 2. The Z-algorithm (Section 3.3) is a simple string matching algorithm that runs in time linear in the length of the text [99]. 3. A suffix tree (Section 3.6) is a data structure for indexing a text. Once this data structure is built, it has the astonishing property that a text can be searched in time proportional to the length of the pattern. Building the suffix tree itself only takes time proportional to the length of the text, but the factor of proportionality is rather large [249, 99]. To demonstrate the time requirements of the three algorithms the program displays the time taken for searching and—with the exception of the na¨ıve algorithm, which lacks preprocessing—the time taken for preprocessing. The text window can be freely edited. In addition, three chunks of interesting prose are provided: 1. The sequence of the first genome to be sequenced, that of the bacteriophage X174, which encodes 10 proteins in 5,386 nucleotides [6]. 2. The alcohol dehydrogenase (Adh) gene of Drosophila melanogaster. In 1983 this was the first gene to be subjected to comparative sequencing [152]. 3. The text of the paper by Watson and Crick announcing the double helical structure of DNA [256]. Tutorial Select the genome of X174 and run the pattern search for all three algorithms provided. Observe in particular the variations in preprocessing time.

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A.2.2 Suffix Tree Molecular Biologists often search for sequence patterns in molecular genetic data. Consider for example the text ACCGTC and the pattern C. A string search should tell us as efficiently as possible that occurs in at positions 2, 3, and 6. Classical results in computer science have lead to algorithms that achieve this in time proportional to the length of . Suffix trees are an ideal data structure for improving on this result in cases where the text is stable and needs to be searched repeatedly. Suffix trees are an index structure of the text, which can be built in linear time [249]. Once built, the text can be searched for a pattern in time proportional to the length of the pattern rather than in time proportional to the length of the text (Section 3.6). Description of Program The program draws a suffix tree for any input string. On this suffix tree it optionally displays four features: 1. The leaf labels, which are the starting positions of the suffices constructed by concatenating edge labels from the root of the tree to the respective leaf. A suffix ACCGTC starts somewhere inside and ends at the end of , for of text example the suffix GTC starts at position 4. 2. The edge labels, which return one of the text’s suffices when read from the root to a leaf. 3. The string depth, which indicates the length of the string stretching from the root to the node. 4. The suffix links, which make it possible for a suffix tree to be constructed in linear time [249, 99]. Tutorial 1. Enter a single letter and construct the corresponding suffix tree. Notice that the program always adds the sentinel character $ to a string before processing it. 2. Add a second letter to the input string that is different to the first letter and construct the suffix tree. 3. Continue adding different letters. Notice that the program is case sensitive. 4. Add a repeated letter and observe the effect on the tree topology. 5. Construct a suffix tree from a string with lots of repetitions. Search it for a pattern by matching from the root. If the pattern is found in the tree, its start positions in the text are given by the leaves in the subtree marked by the end of the match. 6. Notice that the string read from the root to any internal node corresponds to a repeated string in the input sequence. The length of this string is indicated by the String Depth. Look for the longest repeat in the input string.

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A.2.3 Repeat Searching Inexact repeats are ubiquitous in complex genomes such as those of mice [40] and men [128]. In addition, exact repeats between two sequences can be used for constructing fast alignment algorithms [48]. As a result, there is a lot of interest in algorithms for detecting repeats in strings (Sections 3.9 and 3.10). Description of Program The program uses a suffix tree for locating either repeats of a pre-defined length or for identifying the longest repeat. Sets of repeats are marked by the same background color. The text window can be freely edited. In addition, three chunks of interesting prose are provided: 1. The sequence of the first genome to be sequenced, that of the bacteriophage X174, which encodes 10 proteins in 5,386 nucleotides [6]. 2. The alcohol dehydrogenase (Adh) region of Drosophila melanogaster. In 1983 this was the first gene to be subjected to comparative sequencing [152]. 3. The text of the paper by Watson and Crick announcing the double helical structure of DNA [256]. Tutorial 1. Look for the longest repeat in the double helix paper. 2. Look for the next longest repeat in that paper. Is the repeat length correlated with the phrase’s importance? A.2.4 Hash Table Hash tables are generalized arrays, where instead of using integers as indices, arbitrary objects are utilized. Such indices are usually referred to as keys and the object accessed though a key as its value. The hashing of query sequences into words of a predefined length is an important step in some fast database search tools, including FASTA [197]. Description of Program The program takes as input an arbitrary string and computes a table of words of a certain length and their positions in the text. The word length can be varied by the user. The program also displays an indexed version of the input string for easy comparison with the table entries.

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Tutorial 1. Hash the default sequence into words of length 1. 2. Hash the sequence into words of increasing length. What is the longest repeat in the input sequence? 3. Enter a short English sentence (e.g. O, take the sense, sweet, of my innocence!) and hash it into words of length 3 by either pressing Enter or clicking >. Do you find a repeated hash key? Compare the result with a hash of MADAMIMADAM. A.2.5 Dotplot A dotplot is a simple means of comparing two strings (Fig. 4.6). A simple generalization of the kind of dotplot shown in Figure 4.6 is achieved by plotting matches and this approach has been used to compare sequences on a of some length genomic scale [233]. In addition, dotplots are used to visualize the FASTA search strategy (Section 4.2.2). Description of Program The program takes two sequences as input and displays matches of a certain length. The user can enter new input sequences and vary the length of the matches displayed. In addition, the user can randomize the input sequences to observe the length of matches between random sequences. Tutorial 1. Click > to draw a dotplot of the two alcohol dehydrogenase sequences supplied by the program using a Match Length of 11. 2. Observe the matches, indicated as black lines, along the diagonal. 3. Notice the gap in the matches, which is caused by the insertion of the copia transposon in Sequence 1 at that position (cf. header line of Sequence 1). 4. Randomize the two sequences by clicking the die and redraw the dotplot. 5. Vary the Match Length to find the longest match between the two randomized sequences.

A.3 Probability Biological data is often described as “noisy”. Its analysis is therefore routinely coupled to probabilistic considerations. The following program demonstrates the application of probability theory in hidden Markov models.

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A.3.1 Hidden Markov Model Hidden Markov models are probabilistic models of sequential data (Section 6.2). Such sequential data could be a stream of phonemes uttered by a speaker or a stretch of nucleotides. In the case of nucleotides we might be interested in inferring the position of genes along the sequence. Say we knew that protein coding regions were G/C-rich compared to non-coding regions. Then we could model our stretch of DNA by postulating two hidden states corresponding to coding and non-coding. These two states emit nucleotides with different probabilities. Moreover, given that the system is in the coding state, it changes into the non-coding state with some probability and vice versa. The set of transition probabilities between the hidden states and emission probabilities attached to each of the hidden states is called the hidden Markov model. Given such a model and an unannotated stretch of sequence, we can try to guess the locations of coding and non-coding regions. If, on the other hand, we do not know the probability parameters of the model, we can try to infer the model from some “training” data using an arbitrary model as our starting point. Description of Program The program allows the user to manipulate the probabilities of a hidden Markov model of a DNA sequence. The model consists of two hidden states and two observable states. The observable states are A/T and G/C, while the hidden states are indicated solely by different colors. Given this model, the user can generate a DNA sequence with hidden states indicated by color coding. Conversely, the program can guess the most likely sequence of hidden states given simulated DNA sequence data. The inferred hidden states can then be compared to the known states. In addition, the program can take a simulated DNA sequence and a random hidden Markov model as input and by an iterative procedure infer the most likely model. Tutorial 1. Detect Hidden States Tab a) The hidden Markov model on the left consists of a starting point, which changes into one of the two hidden states shown as colored panels with the probabilities indicated on the transition arrows. The hidden states each contain a histogram with two bars indicating the probabilities with which they emit either an A/T or G/C. When emitting A/T, the model emits with equal probability an A or a T, and similarly for G/C. Once the model is in a certain hidden state, it either stays in that state with the probability noted next to the self-referential arrow or changes to the other state with the probability noted next to the arrow pointing to the alternative hidden state. All ten probabilities displayed on the model can be manipulated with the five sliders on the right. Try out all five sliders and observe the effect. b) Reset the model by clicking the reset button.

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c) Generate a DNA sequence by clicking >. This sequence appears in the lower panel on the lefthand side with the hidden states marked by the same colors as they appear in the model. d) Estimate the sequence of hidden states from the data by clicking ?. The DNA sequence with the estimated annotations appears in the panel on the bottom righthand side. Compare the known hidden states with those estimated from the data. 2. Estimate HMM Tab a) There are three hidden Markov models displayed here; from left to right they indicate: The model generated in the Detect Hidden States tab. A random initial model. The model yet to be estimated. This model is initialized to the same values as the random model on its left. b) Generate a new DNA sequence according to the model displayed on the left by pressing >. c) Estimate a new model by pressing ?. This may take some time. d) Compare the estimated model to the random initial and the true models. Did you get a good fit? e) Generate a new random initial model by clicking the die and repeat the model estimation. You will find that model estimation depends to some extent on the initial model. The reason for this is that the estimation algorithm, also known as Baum-Welch algorithm, can easily converge on a local optimum rather than the global optimum, which should be very similar to the true model.

A.4 Evolution The pattern of polymorphisms detected when comparing homologous sequences is the product of evolution. The following programs demonstrate the construction of phylogeny as well as the forward and reverse simulation of genes in populations. A.4.1 Phylogeny The human interest in reconstructing the ancestry of powerful families is at least as old as the oldest literary documents. For example, Genesis 5 lists the descendants of Adam down to Noah and Genesis 10 recounts the descendants of Noah’s three sons, who go on to populate the earth. The preceding description of creation (Genesis 1) is remarkably free of such genealogical thinking. However, at its most basic level a phylogeny is simply the generalization of a family tree (Chapter 8).

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Description of Program The program takes as input a set of aligned sequences in FASTA format and seven primate mitochondrial DNA sequences are provided as default input. The program computes the pairwise distances between these sequences. Users can choose between three different distance measures: 1. number of mismatches; 2. normalized number of mismatches, that is, the number of mismatches divided by the length of the alignment; 3. Jukes-Cantor distance [130]. A simple phylogenetic tree is reconstructed from these distances using the UPGMA (unweighted pair group method with arithmetic means) method (Section 8.4.1). In addition, the program demonstrates the application of the bootstrap in phylogeny reconstruction (Section 8.8). Tutorial 1. Distance Matrix Tab a) Compute the pairwise number of Mismatches between the displayed sequences by pressing >. Notice that the first number in the panel containing the distance matrix is the number of taxa analyzed. This corresponds to the file format used in PHYHLIP, a widely used package for calculating phylogenies [72]. b) Count the number of mismatches between the human and chimpanzee sequences and compare your result with the corresponding entry in the distance matrix. c) Compute the Normalized Mismatches and make a note of the distance between human and gibbon. d) Compute the Jukes-Cantor distance. Compare the Jukes-Cantor distance between human and gibbon with the normalized number of mismatches for this pair of taxa. 2. Phylogeny Tab a) Compute the phylogeny from the distance data (>>). b) Display the branch lengths on the tree. c) Compare the branch lengths with the distances in the distance matrix. 3. Bootstrap Tab a) Generate a bootstrap sample from the original data. This is done by sampling with replacement columns from the original alignment. b) Compute the distance matrix for the bootstrap data (>>). c) Generate the phylogenetic tree for the new distance matrix (>>). Is the tree topology the same as for the original data set? d) Repeat the process of bootstrapping and observe the range of tree topologies generated (>>). e) Click the gearwheel to animate the generation of bootstrapped trees. Notice that you can vary the animation speed using the Step Time slider.

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A.4.2 Drift Random changes of allele frequencies during evolution are called drift. This contrasts with changes in allele frequencies due to a host of other possible factors, the most interesting of which is selection. Since drift is easy to model, it often serves as a point of departure for an investigation of the evolutionary forces acting on a population (Section 10.3). Description of Program The program consists of two modules, Drift without Mutation and Drift with Mutation. 1. Drift without Mutation: This demonstrates evolution in a two-allele system with alleles and . The user can choose a population size and an initial frequency of the -allele. By pressing the Run-button (>) the simulation starts showing the frequency of the -allele and the genetic diversity, , as a function of time measured in generations. Repeated pressing of the Run-button launches new runs of the simulation and a concomitant entry in the legend that displays the population size. All simulations can be stopped by pressing the Stop-button and the panel is reset by pressing the Reset-button. If a simulation runs off the graph, you can still follow it by dragging the graph panel with the left mouse button to the left. You can also zoom into the graph by selecting a region of interest; to zoom out again the same action of dragging the graph panel with the left mouse button to the left is used. Furthermore, by right-clicking the graph a number of layout options are opened. 2. Drift with Mutation: Here the user can set a population size and a mutation . Pressing the Run-button starts the simulation under rate ranging from to the infinite alleles model. The genetic diversity of the population is shown as a function of time in generations. In addition, the expected genetic diversity is also shown. Each click on the Run-button starts a new simulation, while pressing the Stop-button stops all of them. The Reset-button returns the panel to its initial state. Tutorial 1. Drift without Mutation Tab a) Using the default parameters, start a simulation run (>). Notice that the ) or extinction allele quickly goes either to fixation ( ) and that both events correspond to a genetic diver( sity of zero. and observe the frequency of runs that b) Set the initial frequency of to . go to fixation. Compare this with

A.4 Evolution

271

c) Reset the program, increase the population size to 100, set , and start a new simulation run. This time it takes longer on average for to either reach fixation or go extinct. and observe the d) Leave the population size at 100 but vary frequency of fixation in a number of runs. 2. Drift with Mutation Tab a) Using the default parameters, start one simulation run (>). Recall that without mutation the frequency of a given allele eventually either reaches unity (fixation) or zero (extinction). Both values correspond to a genetic diversity of zero at which the locus in the population gets stuck in the long term. In contrast, in the presence of mutation, genetic diversity can always recover from a crash to zero as long as the mutation rate, , is greater than zero. b) Continue to observe the first simulation run and notice the horizontal line indicating the expected genetic diversity. This is computed as

where is the population size. Here is multiplied by rather than by , because the program simulates a haploid population, where the population size is equal to the number of genes. c) Stop the present simulation and reset the program. Change the population and , and start another size and mutation rate to, say, simulation run. Notice two things: (i) the observed genetic diversity fluctuates strongly around its expectation and (ii) the values of when going from one generation to the next tend to be similar to each other rather than being drawn randomly from the full distribution of possible values. The values of are therefore said to be autocorrelated. A.4.3 Wright-Fisher The Wright-Fisher model of neutral evolution consists of a population of constant size that evolves by resampling with replacement. In its original formulation this model moved forward in time (Section 10.3). However, it can also be analyzed backward in time and this perspective forms the basis of coalescent theory (Section 11.3). The aim of this module is to visualize descendants and ancestors under the WrightFisher model using the original one-parent genealogy as well as the more familiar, though genetically largely irrelevant, two-parent genealogy. Description of Program The program simulates the evolution of a Wright-Fisher population. It displays the evolving entities and the lines of descent that connect them between the generations. We can think of the evolving entities as homologous genes in the one-parent case or as individuals in the two-parent case. The user can choose between these scenarios, as well as select the number of generations simulated and the size of the population.

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A bioinformer

1. One-Parent Genealogy: By default the program displays a “tangled” version of the lines of descent, which results from letting genes in generation randomly . A disentangled version of pick their parent (one per gene) in generation this graph is generated by checking Disentangle. The offspring of individual genes can be visualized forward in time by clicking genes in generation 1. Similarly, the ancestry of individual genes is displayed by clicking them in the present generation. In coalescent theory the most recent common ancestor of a sample of genes is the point at which the simulation stops. In our program the most recent common ancestor of the selected genes is marked in red. Note that it is possible that a sample of genes has not yet reached its most recent common ancestor. 2. Two-Parent Genealogy: In the two-parent mode only color-coded invividuals are displayed initially. Black individuals are those that have left some descendants in the present generation; red individuals are universal ancestors and blue individuals have left no descendants in the present generation. Like in the one-parent mode, clicking on individuals in the present or in the first generation displays an individual’s ancestors and descendants, respectively. Notice that individuals pick their parents randomly in the preceding generation. As a consequence, there is a probability that an individual picks the same parent twice. This corresponds to the original model and can be reconciled with common sense if we reinterpret a dot as a couple rather than an individual [37]. Most couples have two couples as parents, but occasionally the children of one couple mate among each other. Tutorial One-Parent Genealogy 1. Using the default parameters, run the program in the one-parent mode once. Red numbers indicate generations, black numbers label individuals. Follow a few of the descendants of individual genes by clicking on them in generation 1. Notice that in quite a few cases genes leave no descendants in the present. 2. Follow the ancestry of individual genes by clicking on them in the present generation. Observe that the position of the most recent common ancestor shifts depending on the genes selected. 3. Disentangle the lineages. This makes the graph easier to read. Notice also that the order of the individuals in the last generation has changed due to the disentanglement. Again follow the descendants and the ancestors of individual genes. 4. Run a few simulations. Notice that in the disentangled mode it takes on average generations for the rightmost and the leftmost gene in the present generation to find their most recent common ancestor. In contrast, all remaining genes in the population coalesce on average twice as fast. Two-Parent Genealogy 1. Run the program once in the two-parent mode using the default parameters. Notice that the layer of generations containing black dots, i.e. individuals with some

A.4 Evolution

273

descendants in the present, is rather thin. Red dots, that is universal ancestors, appear early on and start to dominate the population further back in time. 2. Click on an individual in generation 1 to visualize its descendants. Notice that blue individuals may or may not have their lineage terminated in generation 1. 3. Click on individuals in the present generation to visualize their ancestors. The ancestors of different individuals start to rapidly overlap. 4. The one-parent and two-parent modes of the program are based on the same underlying population. Hence by switching between the two modes, you can observe that universal ancestors need not have contributed genetic material to a given descendant. A.4.4 Coalescent When going backward in time, gene lineages coalesce at the point where they diverged from their last common ancestor. Hence the tree structure tracing back the genealogy to the most recent common ancestor of a sample of genes is called the coalescent and the associated theory is referred to as coalescent theory (Section 11.3). Description of Program The program’s toolbar allows the manipulation of the sample size and of the population parameter theta ( ). This is the scaled mutation rate

where is the size of a haploid population and the mutation rate. This parameter has a simple relationship to the expected number of segregating sites, , found in a sample of DNA sequences [258]: (A.1) where is the sample size. Notice that for a sample of two sequences the number of segregating sites, also known as single nucleotide polymorphisms, SNPs, is equal to . The program demonstrates the creation of sequence samples with mutations. Tutorial 1. Click on >> to generate a genealogy. Notice the time scale measured in units of generations. 2. Click on >> to throw mutations onto the genealogy. The mutations are displayed as red squares and their average number is given by Equation (A.1).

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A bioinformer

3. Click on >> to generate the haplotypes, or sequences, that correspond to the displayed genealogy with mutations. The sequences are stylized as labeled black lines with the positions of the mutations indicated as before by red squares. Each mutation on the genealogy corresponds to a segregating site, i.e. a polymorphic column of haplotype positions. Notice that mutations affect all the sequences that are located below it in the genealogy. Conversely, mutations on the outer branches of the genealogy only appear in a single sequence. These are also known as singletons and on average they make up

of all the mutations [192]. 4. Click the gearwheel to animate the display of random genealogies. Notice that you can regulate the speed of animation using the Step Time slider.

B Probability

Statistical reasoning as well as simple probability calculations crop up everywhere in computational biology. In the following we give a very brief summary of these topics. A more detailed account can be found in the textbook on statistical bioinformatics by Ewens and Grant [66]. For an introduction to the subject of probability theory the reader is referred to the classical textbook by Feller [68].

Random Events and Their Probabilities Many biological processes have a random element, that is, their outcome is uncertain. But even processes with uncertain outcome can be described quantitatively if we observe enough of them under constant conditions. For example, it is uncertain whether a boy or a girl will be born, but if we observe enough births we find that 51.5% of newborns are boys. An experiment is a repeatable process with uncertain outcome. The outcome of an experiment is called an elementary event. As an example consider the experiment “dice rolling” and let be the set of all possible and mutually exclusive elementary . events of this experiment, that is, Every subset is called an event. An event occurs if and only if one of the elementary events constituting occurs. In the above example corresponds to the event of obtaining a number greater than 2 when rolling a die. Every event has a probability attached to it, . Addition Rule The probabilities of mutually exclusive events are additive. To put this more formally, be random events, for , then let (B.1) which is also known as the addition rule. As an example consider the probability of obtaining a number greater than 2 when rolling a fair die, i.e. one where each

276

B Probability

elementary event has the same probability, to the addition rule, the probability in question is then .

,

. According

Conditional Probabilities The conditional probability is the probability of event given that event has already occurred. This is defined as the probability of both and occurring divided by the probability of : (B.2) have the property that Independent events with Equation (B.2) leads to the multiplication rule

, which together (B.3)

For example, the probability of obtaining an even number and a number greater than . 2 when rolling two dice is

Bayes’ Formula Let the certain event consist of pairwise independent events, that is, and for ; then we can represent any random event as . According to the addition and multiplication rules expressed in Equations (B.1) and (B.3), we obtain from this

The latter expression is also called the total probability of event . Instead of infergiven , which is also ring the probability of we can derive the probability of known as the posterior probability of . This leads to Bayes’ formula: (B.4) As an example of the application of Bayes’ formula consider playing in an occasionally dishonest casino where 99% of the dice are fair, but the remaining 1% return a 6 in 50% of cases. Imagine you roll a die three times and obtain a six each time. Are you dealing with one of the biased dice? “biased die” “3 rolls of 6” Probably not.

B Probability

277

Random Variables and Their Distributions A (discrete) random variable is a numerical quantity, which, as a result of a random experiment, can take any of some discrete set of possible values. For example, the dinucleotides observed in 1 kb of nucleotide sequence is a variable number of that fluctuates randomly between different 1 kb fragments. The possible values in this . Associated with a random variable is its distribuexample range between and tion. It assigns probabilities to the possible (subsets of) values of a random variable. , whereas Random variables are customarily abbreviated by capital letters particular values are usually abbreviated by small letters . Thus, the expresmeans that the “probability that the random variable takes sion value is equal to ”. Important characteristics of a distribution are its moments. The -th moment of random variable is defined as , where the summation ranges over all possible values of . The first moment is also known as the mean or expectation. The variance is the difference between the second moment and the squared first moment. For instance, for random variable with , the variance is . mean In genetics, a frequently encountered distribution is the Poisson distribution. A discrete random variable which can take integer values is said to be Poisson distributed with parameter , if

where is a real number. The mean and variance of a Poisson distributed . random variable are In Chapter 10 we use the concept of conditional expectations. The conditional given that some other random variable takes expectation of a random variable is written as . It is the first moment of under the some value . conditional probability distribution A more complete list of the basic properties of random variables, including continuous random variables, and conditional expectations can be found in the textbook by Karlin and Taylor [136].

Parameters and Their Estimators It is important to distinguish between parameters and their estimators. Parameters are the usually unknown properties of populations. In contrast, estimators are defined with respect to a sample drawn from the relevant population in order to estimate a parameter. For example, the variance of a sample, , is a possible estimator of the population variance .

Exercises B.1. Consider a disease-causing recessive genotype that occurs in one individual out of 10,000. What is the expected frequency of the corresponding disease phenotype?

278

B Probability

B.2. Imagine a genetic defect which increases the risk of colon cancer in old age and which affects one in a million individuals. A test for this mutation has become available recently. This test has a 0.01% false positive rate. Based on Bayes’ formula, you decide not to undergo the test. Why not? B.3. The genome of the mustard weed Arabidopsis thaliana contains approximately nucleotides. Let be the rate of mutation per nucleotide per generation. How many mutations do you expect to occur per generation throughout the genome? B.4. Assuming that the number of mutations is Poisson distributed with a rate of the expected number of mutations just calculated, what is the probability that the genome of A. thaliana has accumulated at least one mutation after one generation?

C Molecular Biology Figures and Tables

This book is centered on the analysis of nucleic acid and protein sequences.We therefore survey in the following a few fundamental properties of the constituents of these sequences, nucleotides and amino acids. For more details on the current state of molecular biology we refer the reader to Watson’s well-known textbook [255], while Judson has written a vivid history of the early days of molecular biology [129]. This history is brought up to date in Watson’s popular survey of all that is DNA [254].

Nucleic Acids DNA and RNA are both nucleic acids and Table C.1 summarizes the nomenclature of their constituents: nucleosides consist of a base and a pentose sugar moiety, while nucleotides consist of a nucleoside plus a phosphate group. The energy-carrying molecule adenosine triphosphate (ATP) is an example of a nucleotide. However, in computational biology “nucleotide bases” is often used synonymously with “nucleotides”. The carbon atoms of the central sugar molecule in nucleotides are numbered as shown in Figure C.1. The phosphate groups link the 3’ and the 5’ carbons of the sugars into long polymers. One corresponding monomer of DNA and RNA is shown in Figure C.2. The main difference between RNA and DNA is that in RNA the sugar moiety has an extra hydroxyl group at the 2’ position. This makes RNA much more reactive than DNA. In addition, uracil replaces in RNA the chemically similar thymine found in DNA. In the cell both molecules can only be synthesized in the direction 5’ 3’. The usual form of RNA is single stranded, while DNA consists normally of two antiparallel complementary strands that assemble by specific hydrogen bonding between A/T and G/C base pairs into the familiar double helix (Figure C.3). When sequencing DNA molecules, it is sometimes not possible to precisely determine the identity of a given base. The set of ambiguity codes shown in Table C.2 is used to describe all possible base identifications.

280


5’

O 4’ 3’

1’ 2’

Fig. C.1. Numbering of carbon atoms in the ribose moiety of nucleotides. A

B

O H

N

O N HO P O O O

O CH

H

N

O N HO P O O O

O

O

OH

Fig. C.2. Example of a monomer of DNA (A) and RNA (B) molecules. The bases can vary from position to position.

Fig. C.3. Structure of DNA.


281

Table C.1. Nomenclature of nucleic acid constituents. The cut-off bond ( ) is attached to the ribose moiety.

Base

Structure

Chemical Class

Nucleoside

Symbol

purine

adenosine

A

purine

guanosine

G

pyrimidine

cytidine

C

pyrimidine

thymidine

T

pyrimidine

uridine

U

NH N

N

adenine

N

N

O H

guanine

N

N

H N

N

N

NH H

cytosine

N N

O

O H

thymine

CH

N

O

N

O H

uracil

O

N N

Table C.2. Ambiguity codes for nucleotides as defined by the International Union of Biochemistry and Molecular Biology (IUBMB). Symbol A B C D G H K M N

Meaning A TGC C TGA G TCA TG CA TGCA

Symbol R S T U V W X Y

Meaning GA GC T T GCA TA TGCA TC

Proteins Proteins consist of amino acids and Table C.3 lists the symbols of the 20 amino acids specified by the genetic code. In Figure C.4 these amino acids are classified according to their physico-chemical properties.

282

C Molecular Biology Figures and Tables Table C.3. The one letter and three letter codes for amino acids. One A C D E F G H I K L

Three Ala Cys Asp Glu Phe Gly His Ile Lys Leu

Name alanine cysteine aspartic acid glutamic acid phenylalanine glycine histidine isoleucine lysine leucine

One M N P Q R S T V W Y

Three Met Asn Pro Gln Arg Ser Thr Val Trp Tyr

Name methionine asparagine proline glutamine arginine serine threonine valine tryptophan tyrosine

Proteins are synthesized by a fusion of the amino and the carboxyl groups of the amino acids concerned. This is depicted in Figure C.5 and leads to the creation of an N-terminus and a C-terminus in the resultant protein. Cellular protein synthesis proceeds in the direction N C as the ribosome moves along the messenger RNA in the direction 5’ 3’.

Genetic Code The genetic code links the information contained in nucleotide sequences to the primary structure of proteins. Table C.4 summarizes the “standard” genetic code. Note that small variations on this standard code exist in the mitochondrial codes of a wide variety of organisms, including members of the metazoans (e.g. vertebrates), fungi (e.g. Saccharomyces spp.), and green plants, including land plants. In addition, variant nuclear codes have been described for members of the fungi (e.g. many Candida spp.), green algae (e.g. Acetabularia spp.), ciliates, diplomonads (e.g. Giardia spp.), and firmicutes (e.g. Mycoplasma spp.) [150]. These variant codes can be looked up at www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi.


H H N

H

H

COOH CH CONH

H N

Asn

COOH CHOH CH Thr H

H N H H N

COOH CH COOH Asp

283

COOH CH OH

H N

COOH H N

Ala H H N COOH H Gly

Pro

H

H H N

Ser

COOH CH

H N

COOH CH SH

H C

COOH CH CH

Cys

tiny

Val

small H H H N

H

COOH CH

H N

COOH CH

H N

N H

H

H N

COOH H CH H N COOH CH

H C

Ile

N His

COOH CH CH COOH Glu

N

Phe

OH Tyr

H H N

COOH CH CH CH CH NH

Lys H H N COOH CH CH CH NH C H N NH

H H N

COOH CH CH H C CH Leu

H H N

COOH CH CH CONH

aliphatic H H N

Gln

COOH CH CH S CH Met

Arg

negative

H

Trp

aromatic

H H N

COOH CH CH CH

positive polar

non−polar

Fig. C.4. Classification of the 20 proteinogenic amino acids according to their physicochemical properties.

H N H N

O OH

O O

OH

N

H N

OH

O

Fig. C.5. Synthesis of a dipeptide from two amino acids. and are side chains drawn from the repertoire depicted in Figure C.4. and refer to the N- and C-terminus of the peptide, respectively.

284


Table C.4. The standard genetic code. If your have trouble remembering the order of the bases, then “Think Carefully About Genes”.

second position 5’ end

T C A G

3’ end

T CAG Phe/F

Tyr/T Cys/C

T C

Ser/S Ter/*

Ter/*

A

Trp/W

G

Leu/L

T

His/H Pro/P

Arg/R

C A

Gln/Q

G Ile/I

Asn/N Ser/S

T C

Thr/T Lys/K Arg/R

A G

Met/M

T

Asp/D Val/V Ala/A

Gly/G Glu/E

C A G

D Resources

In this book we have used and/or referred to a number of key bioinformatics software packages and databases. The ones we use most frequently are listed in Tables D.1 (software) and D.2 (databases). However, our lists reflect only a small portion of what is regularly used by working computational biologists. A good starting point to explore the rapidly changing world of bioinformatics software and databases are the software and database directories maintained by the journal Nucleic Acids Research on their website nar.oupjournals.org. Table D.1. A selection of bioinformatics applications. Program Purpose BLAST Database search clustalw Multiple sequence alignment EMBOSS Various bioinformatics tasks DnaSP Analyze DNA sequence polymorphisms FASTA Database search GenScan Gene prediction gff2aplot Plotting sequence comparisons ms Coalescent simulations MUMmer Genome alignment PHYLIP Phylogeny reconstruction SGP-2 Gene prediction Slam Gene prediction

Reference Website [9] www.ncbi.nlm.nih.gov/BLAST [246] ftp-igbmc.u-strasbg.fr/pub [209] emboss.sourceforge.net [211] www.ub.es/dnasp [197] fasta.bioch.virginia.edu [30] genes.mit.edu/GENSCAN.html [2] genome.imim.es/software [122] home.uchicago.edu/rhudson1 [48] www.tigr.org/software [72] evolution.genetics.washington.edu [196] www1.imim.es/software/sgp2 [193] bio.math.berkeley.edu/slam

Table D.2. A selection of bioinformatics databases. Name GenBank EMBL DDBJ SwissProt PDB INTERPRO

Purpose primary sequence repository primary sequence repository primary sequence repository curated protein sequences macromolecular structures protein motifs

Website www.ncbi.nlm.nih.gov www.ebi.ac.uk/embl www.ddbj.nig.ac.jp www.expasy.org/sprot www.rcsb.org/pdb www.ebi.ac.uk/interpro

sdfsdf

Answers to Exercises

Chapter 2 2.1 2.2

2.3 2.4 2.6 1. First, we compute the matrix , that is, and . Then we compute the rounded log-odds scores, and get and . . For our example, 2. We start by computing and . From this we compute the rounded log-odds scores of and . 2.7 In this case clustering does not change the final result, so the answer is shown in Figure 2.11B. 2.8 Expected score:

The scoring scheme can be applied in local alignment algorithms because the ex. pected score is 2.9 129 2.10

A C G AC CG ACG

2.11 The number of pairs of substrings is

288


2.12 Let be the number of global alignments between two sequences of lengths and . Then the sought number of alignments is

2.13 The hypothetical shotgun sequencing projects has the following parameters: 1. Coverage:

2. Unsequenced nucleotides: 3. Coverage for one unsequenced bp:

. .

2.15 1. ACCGTT-A--GTTCA 2. ACC-GTT----AGTTCA 2.16 Here is an algorithm for calculating the number of cooptimal alignments: Require: //filled dynamic programming matrix Require: //empty dynamic programming matrix Ensure: number of cooptimal alignments do for all do for all //initialize to zero //initialize start point of traceback to do for to do for points back to then if if

points back to

if

points back to

then then

return 2.18 Introduce a new variable, , for cutoff. Let for local alignments and for global alignments. Then we can write the global/local recursions:


289

Chapter 3 3.2 Start with a standard dynamic programming matrix for comparing two sequences. The first row and first column are initialized to zero, matches scored as . Then 1, mismatches as

Now we simply have to find the cell with the highest entry, which gives us the length of the longest exact match between the two sequences. Notice that no traceback is required for uncovering the match, its position as well as its length suffice for . looking it up in the original sequence. This algorithm has a run time . Application of a generalized suffix tree reduces this to

Chapter 4 4.1 Sensitivity = 35/40; specificity = 28/35. 4.2 Here is a summary of the solution strategy:

Path

2

1

5

7

3

4

2

7

2

1

1

1

2

3

2

3

2

4

2

1

2

1

5

7

3

4

2

7

2

1

Start by converting the given sequence of numbers into a graph mapping all possible transitions. As shown in the top row, this graph consists of a set of vertexes, ,

290


and a set of edges, , . Now we fill in the array is the length of the longest subsequence ending at vertex where dynamic programming algorithm to do for

, , using the

In order to actually obtain the set of vertexes corresponding to , we store for each one or more back pointers to (cf. middle row). By starting from the maximal entry in and working backwards we obtain the two cooptimal paths consisting of four nodes each shown in the bottom row. 4.3 Matching in the E. coli genome: 1. Let be the frequency of -th nucleotide and compute , which for our data is . 2. The probability of obtaining a match of length is . The expected number of matches of length between two sequence of length is . In our case . If we make the assumption that the longest match of length occurs only once, we can solve

for

to find

3. 4. 4.6 Base your solution on a generalized suffix tree and consult Section 3.10. 4.7 1. It is a neuronal protein. 2. Yes, using the BLOSUM62 matrix (Table 2.4) we find that the score of alignment EWL/KWL is 16. 3. 59 4.

5. No, we expect approximately 10 such alignments by chance alone. 6. Score: no change as it is independent of database size; -value: doubled, since , i.e. if is doubled while and remain constant, is doubled, too. 4.8 1. 2.


291

Chapter 5 5.1 It implies that this position has been conserved throughout evolution and probably contributes critically to the 3D-structure of the globin protein. 5.3 The desired hyperlattice looks like this: G T G T

A A

A

T

G

5.4 5.5 5.6 Here is an alternative alignment: AATG -ATG --TG 5.7 5.8 5.9

5.11

5.12 A protein sequence is a third as long as the corresponding DNA sequence. The pairwise and multiple alignment phases of the algorithm should therefore become nine times faster. The impact of this on overall performance depends on the relative amount of time taken by the alignment phases and construction of the neighbor ), as well as data loading, etc. joining tree ( 5.13 By following the alternative traceback path we get: AACGT A-CGT A-AGT A--GT

292


Chapter 6 6.1 The desired column in the profile looks like this: Position A C D E F G H I K L M N P Q R S T V W Y 21

4 0 0 0 0 0 1 0 0 0 0 0 0 0 0 2 0 0 0 0

6.2 6.3 Illness:

. ; health:

.

6.4 Here is a possible result: hidden observed n n e 6.5 The next observation state in the sequence is C; given the HMM, we therefore get

6.6 The next symbol in the observation sequence is A; hence, we get the extended Viterbi matrix T G C T A 0.7200 0.1411 0.0277 0.0217 0.0170 0.0300 0.0195 0.0127 0.0035 0.0010

6.7 By writing the corresponding match states into columns we get the following alignment: ATGAA-GA-TG-T 6.8 The induced alignment is ambiguous as the state sequences are compatible both with ATGA A-CA and ATGA AC-A


293

Chapter 7 7.1 All three exons have reading frame 0. 7.2 The remainder of

exons can be computed as

7.3 7.4 Expect donor and acceptor dinucleotides each. 7.5 Recall that TAA, TAG, and TGA are stop codons. Hence we get for the various G/C-contents: (a) (b) (c) 7.6 The average distance in an infinitely long sequence is the mean of the geometric distribution:

If sequences have a finite length , then the upper bound for the exon length is (to accommodate both flanking dinucleotides). At least one acceptor possible start positions. Therefore, dinucleotide has to be found among

For instance, for

, one finds

.

7.7 There are possible nonamers. The number of nonamers with a score larger when scored with matrix from Figure 7.9 is than

Out of these,

nonamers have a score larger than . Therefore, one expects to find

294


donor sites with a positive score in a sequence of kb. The expected number of , which is about times as many as the ones GT dinucleotides is qualified as promising donor sites by the profile matrix.

Chapter 8 8.1 Unrooted: 3; rooted: 15. 8.2 There are six distances between four taxa: 6 4 6 2 6 4 8.3 Here is the phylogeny: 3 2 1 0 1. The phylogeny is rooted: there is a node that denotes the ancestor of the entire clade. 2. The tree is ultrametric. Since the distances fit this tree exactly, they too are ultrametric. 8.4 No; a counter example obtained by permutating the entries in the distance matrix shown in Figure 8.13 would be 1 2 3 4

1 2 6 2

234 2 42 -

8.7 Notice that all polymorphic positions in the alignment except position 7 are singletons. These are not informative for maximum parsimony evaluation, as they add one mutation under any tree topology. The best tree under maximum parsimony can therefore simply be found by considering position 7; it is ((A,B)(C,D)) and its . length trees, 8.8 Remember that nearest neighbor interchange will generate . The latter cannot all be distinct as there pruning and regrafting are only topologies to choose from.


295

Chapter 9 9.2 See p. 432 in the population genetics textbook by Crow and Kimura [43]. 9.5 percentage of transitions and

, where

represent the

that of transversions.

Chapter 10 10.2 Bacteria are haploid organisms. While we can still compute the probability that two genes drawn at random from the population are different, calling this quantity “heterozygosity” is strictly speaking a misnomer. Hence, the “virtual”. 10.3 Solve

for

to find

.

10.4

10.5

10.7

10.8 Use Equation (10.25) and solve

for

to find

.

10.9 Homozygosity is a quadratic function in the allele frequencies, therefore there is only one global minimum. Show by induction on the number of alleles that the , for . minimum is attained at

Chapter 12 12.1 12.2 There are

, external and

12.4 Here is one possible toplology:

internal branches.

296


3

4

1

5

2

12.5 The position of the groups of mutations along the sequences is entirely arbitrary. One of the infinitely many alternative arrangements might look like this:

1 2 3 4 5 12.6 Again, there is a large number of possibilities, for example:

3

4

1

5

2

12.7 Reciprocal recombination would generate AC and TG. 12.8 The graphic below shows under A the matrix classifying all pairs of loci according to whether or not all four alleles are present; the corresponding list of intervals (B) is searched for overlaps, leaving four intervals (C). This means that .

Answers to Exercises A 123456 1 - 11011 2 - 0110 3 -110 4 -11 5 -1 6 -

B

297

C

12.9

Appendix B B.1

B.2 The probability that you are ill given a positive test result is

B.3 Multiply the genome length by the mutation rate:

B.4 Assuming that mutations are Poisson distributed the probability of obtaining mutations is the complement of obtaining no mutation:

sdfsdf

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sdfsdf

Glossary

additive distances: Pairwise distances between taxa possibly evolving at different rates. affine gap cost: Gap costs consisting of a constant part and a part that scales linearly , where is the gap opening cost, the gap with gap length: extension cost, and the gap length. alignment: An alignment consists of two or more sequences written on top of each other in such a way that either residues are paired with residues or with gaps representing insertions/deletions. The aim of alignment construction is usually to align homologous residues. allele: A particular version of a gene. analogy: Similarity between biological traits not based on common descent. bioinformatics: Discipline at the interface between molecular biology and computer science established in the wake of the human genome project. BLAST (Basic Local Alignment Search Tool): Software package for rapidly finding local alignments between a query (pattern) and a subject (text) sequence. In practice the subject often consists of a large set of sequences. BLOSUM substitution matrices: Series of BLOcks SUbstitution Matrices containing scores for all possible pairs of amino acids. A matrix entry consists of the logarithm of the ratio between the probability of finding a pair of amino acids due to homology and the probability of finding it by chance alone. Hence, the scores are also known as log-odds scores. The BLOSUM series is based on blocks of protein sequences that can be aligned without gaps. Sequences with an percent are grouped together resulting in a BLOSUM matrix. identity of bootstrap: Statistical procedure for simulating the process of repeatedly drawing samples from a population. This is done by drawing with replacement elements from a sample of size . When applied to phylogenetic analysis, the result consists of a phylogeny where numbers next to nodes indicate the frequency of obtaining the particular cluster in trees constructed from bootstrap samples of the input data. breadth-first traversal: Tree traversal where sibling nodes are visited before child nodes.

314

Glossary

cDNA: Complementary DNA. DNA molecule derived from a mRNA by reverse transcription. centromere: Region in a chromosome where the spindle apparatus attaches during mitosis and meiosis. Often, but not always situated in the center of a chromosome and made up of repetitive, gene-poor sequence. chromosome: Genomes are divided into one or more chromosomes, each consisting of one long DNA molecule and associated packaging proteins. coalescence: The merging of two lineages in a coalescent when moving from the present into the past, i.e. from the tips of the coalescent toward its root. coalescent: Gene genealogy used for the simulation of population genetic data. codon: Triplet of nucleotides that codes for a single amino acid or stop. DDBJ (DNA Data Bank of Japan): Comprehensive public nucleotide sequence database maintained by the Japanese National Institute of Genetics. Its function is similar to that of GenBank and EMBL. depth-first traversal: Tree traversal where child nodes are visited before sibling nodes. diploid: In a diploid organism such as humans each cell carries two copies of each chromosome. disequilibrium mapping: Searching for genes by looking for associations between a phenotype and anonymous genetic markers. This marker should be in linkage disequilibrium with the desired gene and therefore close to it. DNA (DeoxyriboNucleic Acid): Doublehelical molecule serving as repository of genetic information in the vast majority of organisms. By convention a given DNA sequence, e.g. AGT, is written in the 5’ to 3’ direction. dotplot: A plot for comparing two sequences, each drawn along the edge of a twodimensional matrix. Wherever the two sequences match, a dot is drawn. dynamic programming: Computational method for solving recursive optimization problems by starting at the boundary conditions and storing subproblem solutions rather than recalculating them. EMBL: European Molecular Biology Laboratory. Also the name of the comprehensive public nucleotide sequence database maintained by the European Bioinformatics Institute (EBI) in Cambridge, UK. Its function is similar to that of GenBank, and DDBJ. EST (expressed sequence tag): A sequence tagged site derived from a cDNA. eucaryote: Unicellular or multicellular organism where each cell contains a nucleus. exact matching problem: The problem of finding in a text all the positions where an exact copy of a given pattern starts. exon: Exons are the protein-coding regions of a primary transcript. FASTA: Computer program for rapid local alignment; similar to BLAST. fixation: Used in population genetics to describe the process whereby an allele becomes the only version of the corresponding gene in the population. GenBank: Comprehensive public nucleotide sequence database maintained by the American National Center for Biotechnology Information (NCBI). Researchers

Glossary

315

reporting results based on new sequence data are usually required to deposit their sequence data either in GenBank, EMBL, or DDBJ before publishing their work. gene: Transcribed stretch of DNA, possibly including its regulatory regions. genetic diversity: The genetic diversity of a locus is the probability that two alleles of that locus randomly drawn from a population are different. genetic drift: Random changes of allele frequencies in the course of evolution. genetic marker: A genetic landmark of known position along a chromosome. Such a landmark might, for example, be a gene for white eye color in the fruit fly, or a sequenced stretch of DNA. genome: The entirety of an organism’s genetic information. genomics: The study of whole genomes. global alignment: An alignment strategy aimed at comparing the input sequences along their entire length. Based on the assumption that homology extends along the entire length of the sequences compared. guide tree: In the context of multiple sequence alignment a guide tree is a binary tree with sequence designations at its leaves. Starting from the leaves and working toward the root, this tree specifies the order in which clusters of genes should be pairwise aligned in order to construct a multiple alignment. haploid: In a haploid organism each cell carries just a single copy of each chromosome. Many asexual organisms, including most procaryotes are haploid. hash table: Generalized array where any object, e.g. a string, can serve as index. heterozygosity: The fraction of loci with distinct alleles in a diploid organism; more generally also used as a synonym of genetic diversity. heuristic alignment: Alignment strategy using approximate procedures for obtaining an algorithm that is faster than optimal alignment algorithms. BLAST and FASTA are well-known examples of programs based on heuristic alignment procedures. hidden and obhidden Markov model: Stochastic model consisting of servable states. Hidden states can change into each other with probabilities also specified in the model. A given hidden state emits the observable states with characteristic and predefined probabilities. A typical application of hidden Markov models is gene prediction where the observable states are the nucleotides. The hidden states to be inferred from the observable states might include intron, initial exon, internal exon, final exon, intergenic region, etc. hitchhiking: In the context of population genetics this refers to the process whereby genetic diversity linked to an advantageous allele “hitches a ride” to fixation. homology: Similarity between biological traits based on common descent. inexact matching problem: The problem of finding in a text all the positions where an inexact copy of a given pattern starts. Inexactness might be restricted to mismatches or might include insertions/deletions. infinite alleles model: Model of evolution where each mutation at a locus generates a new allele. If a sequence of, say, 500 nucleotides is considered a locus and the sequence as its allele, then the infinite alleles model is likely to apply.

316

Glossary

infinite sites model: Model of evolution where each mutation at a locus affects a different site. This models mutations in a DNA sequence of reasonable length, where the probability of two mutations affecting the same residue is negligible. intron: Introns are regions of a primary transcript that are not protein coding; they are removed by splicing. Introns are common in eucaryotic genes but very rare in procaryotic genes. keyword tree: Data structure for simultaneously searching a set of key words in a text. linkage: Loci residing on the same chromosome are said to be linked. linkage equilibrium: Loci that are statistically independent of each other are said to be in linkage equilibrium. This amounts to the requirement that the frequency of a genotype consisting of alleles at two or more loci is equal to the product of the alleles’ frequencies. linkage disequilibrium: The absence of linkage equilibrium. Due to reciprocal recombination, linkage disequilibrium between two loci decays with distance. local alignment: An alignment strategy aimed at detecting homologous regions between the input sequences. locus: A position on a chromosome, often synonymous with gene. match: In the context of string searching two identical characters are said to form a match. maximum likelihood: Framework for statistical inference. In the context of maximum likelihood phylogeny reconstruction it provides the criterion for evaluating phylogenies by looking for the tree that maximizes the likelihood of the observed data. maximum parsimony: Criterion for evaluating phylogenies by minimizing the number of evolutionary changes (usually mutations) they imply. microsatellite: Tandem repeats with short repeat units, e.g. GC . Frequently used as genetic markers. mismatch: In the context of string searching two distinct characters are said to form a mismatch. molecular clock: Evolutionary scenario in which the rate of evolution along all lineages is equal. molecular evolution: The study of evolution at the molecular level, i.e. by analyzing DNA and protein sequence data. sequences. multiple alignment: Alignment between mutation: Random change in a protein or nucleotide sequence. neighbor-joining: Method for reconstructing a phylogeny from additive pairwise distance data. neutral evolution: Evolutionary change without fitness effect. non-synonymous substitution: Substitution in a protein-coding DNA sequence that changes the encoded protein sequence. open reading frame: Segment of a nucleotide sequence ranging from a start to a stop codon.

Glossary

317

optimal alignment: Alignment method guaranteed to find the best possible alignment under the model. In practice this is usually based on dynamic programming. ORF: Open Reading Frame. orthology: Similarity between homologous biological traits carried by different organisms. overlap alignment: Alignment strategy for overlapping sequences. Applied in the assembly of sequence fragments generated in the course of a shotgun sequencing project. pairwise alignment: Alignment between two sequences. PAM substitution matrices: Series of Percent Accepted Mutations matrices containing scores for all possible pairs of amino acids. A matrix entry consists of the logarithm of the ratio between the probability of finding a pair of amino acids due to homology and the probability of finding it by chance alone. Hence, the scores are also known as log-odds scores. paralogy: Similarity between homologous biological traits carried by the same organism. pattern: In the context of string searching this refers to the string searched for in a text. PCR (Polymerase chain reaction): Procedure for rapidly increasing the concentration of a specific stretch of DNA sequence in a reaction mixture by orders of magnitude. PDB (Protein Data Base): Public collection of three-dimensional protein structures. phylogeny: Species genealogy, usually computed from empirical data. polyploid: In a polyploid organism each cell carries multiple copies of each chromosome. For example, in hexaploid wheat each chromosome is present in six copies. population genetics: Study of evolutionary forces acting at the level of biological populations. profile: Also known as position-specific weight matrix. Used to represent protein families. procaryote: Organism without a nucleus; all procaryotes are unicellular. protein family: Group of proteins with similar functions. protein: Macromolecule consisting of amino acids, the sequence of which determines its three-dimensional shape and, hence, its function. Their amino acid sequence is specified in their gene’s DNA sequence. By convention a given protein sequence, e.g. MTY, is written in the -terminal to -terminal direction. proteomics: The study of the entire set of proteins from one or more organisms. reading frame: One of six possible ways in which a DNA sequence can in theory be translated by starting at positions 1, 2, or 3 on the forward strand or at positions 1, 2, or 3 on the reverse strand. recombination: Exchange of genetic material between homologous chromosomes during crossing over in meiosis. Reciprocal recombination refers to the process whereby after recombination both participating chromosomes are changed

318

Glossary

downstream of the recombination point. In contrast, gene conversion leaves one of the participating chromosomes unchanged. recursion: A function expressed as a function of itself; for example, the -th Fi-th and the bonacci number is expressed recursively as the sum of the th Fibonacci number: for with the boundary conditions . RNA (RiboNucleic Acid): Single stranded molecule serving as intermediary between DNA and protein. Some RNAs also possess catalytic activity and complex with proteins to form ribozymes, the most important of which is the ribosome, the site of protein synthesis. By convention a given RNA sequence, e.g. AGU, is written in the 5’ to 3’ direction. rooted tree: Refers to a phylogeny with known position of the last common ancestor. selective sweep: The evolutionary process during which an advantageous allele gets fixed in a population. sensitivity: In the context of gene prediction or database searching this refers to the fraction of true targets returned. shotgun sequencing: Method for sequencing long sequences by analyzing a large number of small random fragments and automatically assembling these. SNP (Single Nucleotide Polymorphism): Detected by analyzing homologous positions in two or more DNA sequences. If a position is occupied by more than one nucleotide a SNP has been found. specificity: In the context of gene prediction or database searching this refers to the fraction of true predictions or database hits. splicing: Removal of introns from primary transcripts. In eucaryotes this takes place in the nucleus and the spliced RNAs are exported to the cytoplasm where they are translated. STS (Sequence Tagged Site): A molecular marker consisting of a stretch of perhaps 100 nucleotides of known sequence that is unique within the genome. Often used for genome mapping. substitution: Mutation that leads to an observable allele. suffix tree: Data structure for indexing a text such that it can be searched for a pattern in time proportional to the length of the pattern irrespective of the length of the text. SwissProt: Reference database of protein sequences maintained by the Swiss Institute of Bioinformatics and the European Bioinformatics Institute (EBI). synonymous substitution: Substitution in a coding DNA sequence that leaves the encoded protein sequence unchanged. taxon: A group of organisms, a taxonomic unit. telomere: Terminal regions of chromosomes for protecting the ends of linear chromosomes consisting of a characteristic tandem repeat, for example TTAGGG . text: In the context of string searching this refers to the string in which a pattern is to be found.

Glossary

319

traceback: In the context of optimal alignment, this refers to the process of extracting the alignment from the filled in alignment matrix by following the back pointers specified during the forward phase of the algorithm. transition: Mutation among members of the same chemical class of nucleotides, i.e. among purines (A, G) or among pyrimidines (C, T). transversion: Mutation between members of the chemical classes of nucleotide bases, i.e. between purines (A, G) and pyrimidines (C, T). ultrametric distances: Pairwise distances between taxa evolving at the same rate. unrooted tree: Phylogeny with unknown position of the last common ancestor. UPGMA (Unweighted Pair-Group Method using an arithmetic Average): Method for reconstructing a phylogeny from ultrametric pairwise distance data. weight matrix: Matrix of the frequency (or some function thereof) of each possible residue along the aligned members of a protein family.

sdfsdf

Author Index

Aguadé, M. 245, 257 Aho, A. 49 Altschul, S. F. 14, 82 Avery, O. T. 117 Baltimore, D. 70 Barker, W. C. 65 Bianchi, N. O. 179 Brenner, S. 11 Burge, C. 139 Burset, M. 121 Cantor, C. R. 151, 152, 160, 161, 174–176, 178, 180, 181, 186 Chakravarti, A. 169 Chomsky, N. 11 Clark, A. G. 215 Corasick, M. 49 Crick, F. H. C. 63, 101, 117 Crow, J. F. 206, 207, 211 Darwin, C. 191, 233 Dayhoff, M. O. 18, 65 Doolittle, R. F. 70, 97 Durbin, R. 114 Efron, B. 165 Ewens, W. J. 209–211, 215, 245, 275 Feller, W. 275 Felsenstein, J. 160, 165, 167 Feng, D.-F. 97 Fisher, R. A. 160, 187, 189, 191, 194, 202, 213, 253

Fitch, W. M. 157 Fu, Y.-X. 251, 252, 257 Fuerst, P. A. 207 Gilbert, W. 16 Gillespie, J. H. 187 Grant, G. R. 275 Griffith, F. 117 Griffith, R. C. 235 Gusfield, D. 62 Haldane, J. B. S. 192, 202 Hardy, G. H. 196, 197, 216 Hartl, D. L. 215 Hein, J. 242 Holmes, E. C. 167 Hudson, R. R. 235, 242, 245, 257 Jukes, T. H. 117, 151, 152, 160, 161, 174–176, 178, 180, 181, 186, 192 Kaplan, N. L. 235 Karlin, S. 82 Kendrew, J. C. 91 Kimura, M. 172, 174, 180, 186, 192, 200, 206, 207, 211 King, J. L. 117, 143, 192 Kingman, J. F. C. 217 Knuth, D. E. 46 Kreitman, M. 137, 189, 245, 253, 257 Lewontin, R. C. 245 Li, W. 80, 140, 251, 252, 257 Li, W.-H. 185 Lipman, D. J. 14

322

Author Index

Majoram, P. 235 Maynard Smith, J. 7 McDonald, J. H. 253, 257 Mendel, G. 117 Moloney, G. 65 Needleman, S. B. 14 Nordborg, M. 242 Ohta, T.

172, 200

Sanger, F. 16 Shierup, M. 242 Swofford, D. L. 167 Tajima, F. Temin, H. Vogt, P.

248, 249, 251, 253, 257 70 70

Page, R. D. M. 167 Pamilo, P. 179 Pauling, L. 172 Pearson, W. R. 14 Perutz, M. 91 Provine, W. B. 215

Waterman, M. S. 14, 40 Watson, J. D. 63, 117 Watterson, G. A. 211, 229, 232, 233 Weinberg, W. 196, 197, 216 Wiuf, C. 242 Wright, S. 187, 189, 191, 194, 202, 215 Wunsch, C. D. 14

Rabiner, L. R. Rous, P. 69

Zhang, M. Q. 139 Zuckerkandl, E. 172

107, 109, 114

Subject Index

acceptor site 124 adaptation 214 additive distances 151, 155, 166 adenine 281 Adh locus 263 donor site prediction 130 GenBank entry 119 gene structure 120 genetic diversity 190, 247, 254 affine gap model 36 Aho-Corasick-algorithm 49 alignment 11, 14, 39, 44, 118, 184 gaps 170 global 12, 30, 37, 39, 67, 68, 262 global/local 72 heuristic 13, 67, 69, 79, 86 local 12, 27, 35, 39, 69, 82, 86 multiple see also separate entry, 91 number of alignments 27, 261 optimal 13, 65, 66, 79, 262 overlap 12, 33, 39 pairwise 91, 137, 259 progressive 97, 113 score 15, 21, 25, 30, 38, 83, 86, 262 spliced 135 types of 12 uniqueness 33 variable positions 158 allele 187 fixation of 195 frequency 199, 202 loss of 195, 200 singleton 211

helix 91, 92 amino acids 16, 281 background frequencies 20, 22, 82 nomenclature 282 physico-chemical properties 283 Amoeba dubia 56 analogy 11, 72 Arabidopsis thaliana 35, 256 archebacteria 1 array 74 assembly 34, 57 average linkage clustering 152 back pointer 33 bacteria 1 bacteriophage X174 35, 263 35 balancing selection 249 Bayes’ Formula 276 binary search 55 bioinformer 4, 259 BLAST 76, 79, 86, 135, 285 bit score 85 -value 83 maximal segment pair 76, 79 blastclust 80 BLOCKS database 22 BLOSUM matrix 26, 89 bootstrap 164, 166, 167, 269 boundary condition 27 branch and bound 163 -value paradox

56

324

Subject Index

Caenorhabditis elegans 35 central dogma 1, 3 clustalw 91, 105, 113, 285 coalescence 222 coalescent 222, 242, 251 theory 222, 273 tree depth 225, 226 vs. phylogeny 224 with recombination 235, 236 with selection 231 with selection and recombination codon 6, 16 bias 126, 127, 134 space 17 computational biology 1 conditional expectation 277 coverage 34 cytosine 281

238

deterministic selection equation 213 diffusion process 199 dists1 180 DIVERGE 179 divergence 188, 245, 247, 248 time 172 diversity 245, 247, 248 DNA structure 280 DnaSP 257, 285 domains of life 2 dominance 214 donor site 124, 128 dotplot 73, 266 drift see genetic drift Drosophila melanogaster 12, 35, 137, 247 Adh locus 135, 189 genetic diversity 191 Drosophila simulans 137 dynamic programming 14, 30, 40, 75, 83, 86, 94, 110, 131, 160 effective number of alleles 207 EMBOSS 104, 106, 285 Escherichia coli 68, 86, 256 est genome 134 estimator 277 eucaryotes 1 evolution 3, 217, 268 adaptive 191 non-adaptive 191

of microbes 193 rate of 205 evolutionary relatedness 184 evolvability 7 Ewens’ sampling formula 209 exon 117, 121, 122, 135 chaining 131, 132 reading frame 125 remainder 125 types 126 expectation 277 extreme value distribution 81 FASTA 73, 79, 265, 269, 285 finite alleles model 202, 215 Fitch-parsimony 157 fixation 204, 215, 239, 249 probability of 197, 214 rate of 203, 205 time to 197–199, 203, 214 fixed difference 253 forkhead box protein 2 182, 184 four point criterion 151 FOXP2 see forkhead box protein 2 fruit fly see Drosophila melanogaster G/C-rich region 109, 267 gap 12, 40 affine model 15, 36 linear model 15 GenBank database 66, 119, 120, 126, 171 gene 117, 139 duplication 80, 138 families 79 genealogy 223, 252 knockout 79 models 121 pool 194, 218 singleton 80 gene prediction 117, 138, 139 ab initio 118, 124, 139 accuracy 123 algorithms 120 chimeric 122 comparative 118, 135, 139 joined gene 123 sensitivity 130 sensitivity and specificity 121, 139 specificity 130

Subject Index splice sites 126 split gene 122, 137, 138 genealogy 188, 217, 242 generalized suffix tree 59, 68, 86 GeneSeqer 135 genetic code 284 genetic diversity 187–189, 191, 193, 200, 207, 215, 239 genetic drift 188, 192, 194, 196, 201, 214, 215, 270 genetic hitchhiking 239, 249 genetic load 192 genewise 134 genome 2, 35, 56 GenScan 134, 285 gff2aplot 285 globins 91, 101, 180 alignment 92, 96 vs. 74 phylogeny 102, 143, 144 profile 106 guanine 281 guide tree 98 Haemophilus influenzae 35 Hamming distance 203 Hardy-Weinberg law 197 hash table 74, 265 heterozygosity 193, 200, 207, 208, 230, 232 decay 201 per bp 240 heterozygotes 202 hidden Markov model 101, 106, 108, 114, 134, 267 and sequence alignment 113 detection problem 110 elements 108 forward procedure 109 scoring problem 109 searching for homologous sequences 112 three fundamental problems 109 hitchhiking mapping 242 HKA test see Hudson-Kreitman-Aguadé test HMM see hidden Markov model homeodomain 12 homogeneity 195

325

homology 11, 35, 44, 72, 91, 92, 106, 111, 114, 169, 271 homoplasy 151, 158, 166 homozygosity 206 homozygotes 202 horizontal gene transfer 69 Hox genes 12, 28 human genome 35, 43, 57 human immuno-deficiency virus (HIV) 70 indels 12, 14, 39, 62, 71, 187, 208 index of association 255, 257 infinite alleles model 203, 208, 209, 215 infinite sites model 203, 215, 224, 229, 254 information content 129 interspersed repeats 57 intron 117 phase 125 Jukes-Cantor distance 152, 269 model 151, 160, 174, 176, 178 keyword tree

48, 49, 62, 71, 76

language 182 LIAN 257 likelihood 159 -mapping 149, 166 ratio test 161, 167 linkage 255 disequilibrium 255, 257 equilibrium 246, 255 log-odds score 22, 82, 85 longest common substring 60 Markov chain 107, 114, 197, 198 match 44, 259 maximal repeats 59, 68 maximal unique matches 68 mean fitness 213, 214 microsatellite 56, 189 midpoint rooting 144, 145 minisatellite 189 mismatch 44, 98, 259 mismatch distribution 231 mismatch percentage 172 molecular clock 143, 144, 166, 172 molecular evolution 171

326

Subject Index

moment 277 most recent common ancestor 221, 222, 226 ms 233, 234, 238, 285 msa 96 multiple sequence alignment 91, 98, 101, 107, 111, 114, 145 global 98 heuristic 93, 97 optimal 94 progressive 97 MUMmer 67, 72, 79, 285 Mus musculus 35 mutation 7, 14, 171 advantageous 213 amino acid 19 frequency spectrum 229 models of 201 nucleotide 16 rate of 143, 171, 225 singleton 230, 237, 252, 274 mutation drift equilibrium 192, 203, 215 mutational distance 16, 146 myoglobin 91, 93 natural selection see selection nearest neighbor interchange 162 neighbor-joining algorithm 155, 156, 166 Neisseria gonorrhoeae 256 neutral molecular evolution 192, 201, 207, 215 neutral mutation hypothesis 191, 210, 245, 248 Ewens-Watterson test 211 Fu and Li’s test 251 Hudson-Kreitman-Aguadé test 245 McDonald-Kreitman test 253 Tajima’s test 248 neutral mutation parameter 246 neutral theory see neutral molecular evolution; neutral mutation hypothesis nucleic acids 279 nucleoside 279 nucleotide 279 ambiguity code 281 null distribution 164 number of alleles 205, 210, 211 number of substitutions 181

orthologous sequences 135, 137 orthology 13, 169, 180 outgroup 144, 166 palindrome 73 PAM matrix 21 Pan troglodytes 35 panmixis 194, 197, 217 paralogous sequences 135 paralogy 169 parameter 277 parsimony 157, 163 PCR primers 92 peptide biosynthesis 283 percent accepted mutations 18 PFAM database 113 phage see bacteriophage PHYLIP 285 phylogeny 93, 143, 268 bifurcating 143, 166 counting trees 148 distance methods 150, 166 length of 157, 159, 166 maximum likelihood 159, 161, 166 maximum parsimony 157, 158, 166 quartet analysis 148 rate equality 144 reconstruction 183 rooted 144, 145, 149, 150, 166 topologies 147 tree space 161 unrooted 145, 149 vs. coalescent 224 platelet-derived growth factor 70 point mutation 14, 39 Poisson distribution 83 polymorphism 183, 188, 191, 237, 253 population 187, 271 effective size 208 genealogy 221 genetics 202 position weight matrix 126, 129 prefix 30 primitive donor finder 126, 129 probability 275–276 probability distribution 245, 277 procrustes 134 profile 101, 105, 113 alignment 105

Subject Index analysis 101 hidden Markov model 106, 111, 114 PROSITE database 44 protein 3D-structure 93 biosynthesis 283 domain 99 families 80, 92, 98, 101, 111, 112 primary structure 101 sequence 16 proteome 80 pruning and regrafting 163 psi-blast 106 purine 281 pyrimidine 281 query sequence

74

random event 275 random genetic drift see genetic drift raw score 85, 87 recombination 233, 245, 253, 254 minimum number of events 254, 257 regular expression 44 resampling with replacement 193, 271 without replacement 256 reverse transcriptase 70 ribose 280 Saccharomyces cerevisiae 35, 56 sarcoma virus 65, 69 scaled mutation rate 207, 273 score matrices see substitution matrices scoring scheme 18, 30, 40, 75, 94 segregating sites 224, 225, 229, 233, 238 number of 248, 273 selection 3, 191, 212, 237, 270 background 237 balancing 231, 238 cost of 192 directional 231, 237 diversifying 179 fundamental theorem 214 negative 237 positive 214, 237 purifying 179, 237, 249 weak 214 selective sweep 249, 251

327

sensitivity 77, 121, 130, 139 sequence 171 annotation 118 logo 129, 130 motifs 98 repetitive 56 space 3–6 variation 169, 184 SGP-2 285 shotgun sequencing 34, 35 sickle-cell anemia 237 signature sequences 44 sim4 134 single nucleotide polymorphisms 187, 224, 250, 252, 254, 273 bi-allelic 188 density of 237 Slam 137, 285 SNP see single nucleotide polymorphisms specificity 77, 121, 130, 139 src 70 star phylogeny 146, 237, 239 state transition matrix amino acids 19 nucleotides 174 statistical geometry 145, 146 Streptococcus pneumoniae 117 string 30 string matching 45, 55, 62, 263 exact 43, 44, 86, 265 inexact 43, 44, 61, 62 -error 71, 79 -mismatch 61, 71 na¨ıve 44, 263 set 49 with keyword tree 50 with suffix tree 51, 263 subject sequence 75 subsequence 31, 68, 86 substitution 171, 189, 192 in FOXP2 183 non-synonymous 19, 178, 253 per site 176 rate of 171, 175, 176 ratio non-synonymous to synonymous 182, 185, 253 synonymous 178, 180, 192, 253 substitution matrices 16, 18, 21, 23, 75, 82, 84, 102, 259

328

Subject Index

application of 27, 28 BLOSUM 22, 25, 40, 76, 82, 85, 260 for profile analysis 104 log-odds score 22 PAM 18, 21, 40, 76, 85, 260 PAM vs. BLOSUM 25, 26 substring 30, 35 repeated 57 unique 58 sufficient statistic 211 suffix 30 suffix array 55 suffix tree 51, 59, 62, 264 construction 54 generalized 59 left-divergent node 59 lowest common ancestor 60, 61 string depth 58, 264 string matching with 51 sum-of-pairs score 94 SwissProt database 106 251 Tajima’s target frequencies 85 three point criterion 151 thymine 281 traceback 30, 33, 36

transcript variants 139 transcription signals 124 translation signals 124 transposition 69 tree 46, 47, 55, 222 tree length 235 type III secretion pathway 68 ultrametric distances 151, 154, 166 universal ancestor 217 UPGMA algorithm 144, 153, 155, 269 uracil 281 variance 277 Vibrio 67 Viterbi algorithm

110, 111, 113

wild type 3 wild-type allele 187, 213 Wright-Fisher model 194, 214, 217, 218, 242, 271 absorbing states 195 absorption time 198, 199 lineages 218 yeast

80

Z-algorithm

45, 263

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